Talks | Parallel Computing – Parallel Computing [alpha]

Keynotes
Talks (by topic)
Talks (by date)

M. Aldinucci, HPC-cloud convergence is the missing link between scientific computing and applied-AI, Machine Learning for Astrophysics {(ML4ASTRO)} Catania, Italy: , Keynote talk, June, 2022.
[BibTeX] [Abstract] [Download PDF]

First, HPC infrastructures are embracing GPUs for their superior performance-per-watt ratio against general-purpose multicores. Second, the next-generation scientific workflows are integrating AI-based steps for their accuracy in approximating and analyzing complex phenomena. Third, AI and specifically Machine Learning (ML), is a perfect workload for GPUs in terms of performance and development time. Today, we cannot still close the circle seamlessly running AI-enabled scientific workloads into HPC infrastructures because their system software and development tools are not designed for modern workloads, such as ML frameworks designed for the cloud. HPC-cloud convergence is likely to bridge the gap. In the talk, we will present Streamflow and CAPIO, two development tools for HPC-cloud convergence.

@misc{22:ml4astro,
address = {Catania, Italy},
author = {Marco Aldinucci},
howpublished = {Machine Learning for Astrophysics {(ML4ASTRO)}},
keywords = {keynote, deephealth, eupex, across, eupilot},
month = {June},
title = {{HPC}-cloud convergence is the missing link between scientific computing and applied{-AI}},
note = {Keynote talk},
year = {2022},
url = {https://datacloud.di.unito.it/index.php/s/2SGswkcip7MoMoH},
abstract = {First, HPC infrastructures are embracing GPUs for their superior performance-per-watt ratio against general-purpose multicores. Second, the next-generation scientific workflows are integrating AI-based steps for their accuracy in approximating and analyzing complex phenomena. Third, AI and specifically Machine Learning (ML), is a perfect workload for GPUs in terms of performance and development time. Today, we cannot still close the circle seamlessly running AI-enabled scientific workloads into HPC infrastructures because their system software and development tools are not designed for modern workloads, such as ML frameworks designed for the cloud. HPC-cloud convergence is likely to bridge the gap. In the talk, we will present Streamflow and CAPIO, two development tools for HPC-cloud convergence.}
}

M. Aldinucci, Da HPC4AI al living lab dello spoke FutureHPC del Centro Nazionale HPC, Condivisioni, Conferenza GARR 2022 Palermo, Italy: , Keynote talk, May, 2022.
[BibTeX] [Abstract] [Download PDF]

HPC4AI is an open-access laboratory of the University of Turin open to researchers, students and companies that manages a double pair of systems: a production cloud-HPC system and its twin dedicated to development. The cloud-HPC system is implemented thanks to an extended version of the GARR cloud (OpenStack) and the SLURM workload manager. HPC4AI is specifically designed to support system software development and cloud-HPC convergence tools. Among these streamflow (WMS), jupyter-as-a-service (SaaS), portable-secure-tenant (PasS). The experience gained in the design and management of HPC4AI forms the heart of the design of the livinglab of the Turin “FutureHPC” spoke of the National Center “HPC, BigData and Quantum Computing” funded by the PNRR which should be operational from September 2022.

@misc{22:garr,
optkey = {},
author = {Marco Aldinucci},
title = {Da {HPC4AI} al living lab dello spoke {FutureHPC} del Centro Nazionale {HPC}},
howpublished = {Condivisioni, Conferenza GARR 2022},
month = {May},
year = {2022},
keywords = {keynote, hpc4ai, across, eupex, across, admire, textarossa, eumaster4hpc, icsc},
address = {Palermo, Italy},
note = {Keynote talk},
optannote = {},
abstract = {HPC4AI is an open-access laboratory of the University of Turin open to researchers, students and companies that manages a double pair of systems: a production cloud-HPC system and its twin dedicated to development. The cloud-HPC system is implemented thanks to an extended version of the GARR cloud (OpenStack) and the SLURM workload manager. HPC4AI is specifically designed to support system software development and cloud-HPC convergence tools. Among these streamflow (WMS), jupyter-as-a-service (SaaS), portable-secure-tenant (PasS). The experience gained in the design and management of HPC4AI forms the heart of the design of the livinglab of the Turin "FutureHPC" spoke of the National Center "HPC, BigData and Quantum Computing" funded by the PNRR which should be operational from September 2022.},
url = {https://datacloud.di.unito.it/index.php/s/P3KSroSSmrRxZMc}
}

M. Aldinucci, The modernization of HPC applications for the cloud era, Fifth EAGE Workshop on High Performance Computing for Upstream Virtual event: , Keynote talk, September, 2021.
[BibTeX] [Abstract]

Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g., clouds, supercomputers, and both of them. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments (such as Kubernetes and SLURM), making it possible to execute onto multiple sites not sharing a common data space. Streamflow clearly distinguishes it from many other workflow management systems because it decouples the data dependencies from the deployment of (containerized) workflow steps. Streamflow also leverages CAPIO (Cross-Application Programmable I/O) to move data from one step to another efficiently. CAPIO captures the POSIX file system and streams it in parallel and in-memory to the workflow’s next step, possibly enabling in-transit data filtering.

@misc{21:eni:streamflow,
abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g., clouds, supercomputers, and both of them. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments (such as Kubernetes and SLURM), making it possible to execute onto multiple sites not sharing a common data space. Streamflow clearly distinguishes it from many other workflow management systems because it decouples the data dependencies from the deployment of (containerized) workflow steps. Streamflow also leverages CAPIO (Cross-Application Programmable I/O) to move data from one step to another efficiently. CAPIO captures the POSIX file system and streams it in parallel and in-memory to the workflow's next step, possibly enabling in-transit data filtering.},
address = {Virtual event},
author = {Marco Aldinucci},
howpublished = {Fifth EAGE Workshop on High Performance Computing for Upstream},
keywords = {keynote, streamflow, deephealth, across, admire},
month = {September},
note = {Keynote talk},
title = {The modernization of {HPC} applications for the cloud era},
year = {2021}
}

M. Aldinucci, HPC application cloudification: the streamflow toolkit, PARMA-DITAM (co-localed with HiPEAC) Virtual event: , Keynote talk, January, 2021.
[BibTeX] [Download PDF]

@misc{21:parmaditam:hpc4ai,
address = {Virtual event},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {PARMA-DITAM (co-localed with HiPEAC)},
keywords = {keynote, hpc4ai, deephealth},
month = {January},
title = {{HPC} application cloudification: the streamflow toolkit},
url = {https://datacloud.di.unito.it/index.php/s/HWZijXPqmwfoYCp},
year = {2021},
note = {Keynote talk},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2021_PARMA-DITAM_keynote_HPC-Cloudification.pdf}
}

M. Aldinucci, From skeletons to workflows in the cloud-edge era, 14th Intl. Symposium on High-Level Programming and Applications (HLPP) Virtual event: , Keynote talk, 2021.
[BibTeX] [Abstract] [Download PDF]

Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments and that makes it possible to execute multiple sites not sharing a common data space. StreamFlow supports both task and data parallelism and enables the reproducible and scalable execution of workflows, such as AI pipelines, in hybrid cloud-HPC environments. As a running example, we use the novel “universal COVID-19 pipeline” that explore the whole optimisation space of the training of different DNNs to classify COVID-19 lung lesions.

@misc{21:hlpp:streamflow,
abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments and that makes it possible to execute multiple sites not sharing a common data space. StreamFlow supports both task and data parallelism and enables the reproducible and scalable execution of workflows, such as AI pipelines, in hybrid cloud-HPC environments. As a running example, we use the novel ``universal COVID-19 pipeline'' that explore the whole optimisation space of the training of different DNNs to classify COVID-19 lung lesions.},
address = {Virtual event},
author = {Marco Aldinucci},
howpublished = {14th Intl. Symposium on High-Level Programming and Applications (HLPP)},
keywords = {keynote, streamflow, deephealth, across, admire},
month = jul,
title = {From skeletons to workflows in the cloud-edge era},
url = {https://datacloud.di.unito.it/index.php/s/RyRPjNBse5PKnab},
year = {2021},
note = {Keynote talk},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/RyRPjNBse5PKnab}
}

M. Aldinucci, Mnemocomputing, HLPGPU 2019 (Satellite workshop of HiPEAC 2019) Valencia, Spain: , Keynote talk, 2020.
[BibTeX] [Download PDF]

@misc{20:hlpgpu:mnemocomputing,
address = {Valencia, Spain},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {HLPGPU 2019 (Satellite workshop of HiPEAC 2019)},
keywords = {keynote},
note = {Keynote talk},
month = jan,
title = {Mnemocomputing},
url = {https://datacloud.di.unito.it/index.php/s/8pAGGsw4nEtrtNj},
year = {2020},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2020_HLPGPU_keynote.pdf}
}

M. Aldinucci, Toward Near-Data Processing service computing, Parallel, Distributed, and Network-Based Processing (PDP) Cambridge, UK: IEEE, Keynote talk, 2018.
[BibTeX] [Download PDF]

@misc{18:PDP:NDP,
address = {Cambridge, UK},
annote = {http://www.pdp2018.org/invited.html},
author = {Marco Aldinucci},
date-added = {2021-01-01 18:02:39 +0100},
date-modified = {2021-01-01 18:54:26 +0100},
howpublished = {Parallel, Distributed, and Network-Based Processing (PDP)},
keywords = {keynote},
note = {Keynote talk},
month = mar,
publisher = {{IEEE}},
title = {Toward Near-Data Processing service computing},
url = {https://datacloud.di.unito.it/index.php/s/ePDZ44KSsgXSGHw},
year = {2018},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2018_PDP_keynote.pdf}
}

M. Aldinucci, Streaming in the PGAS Era, International Workshop on Autonomic Solutions for Parallel and Distributed Data Stream Processing (Auto-DaSP 2017), Workshop of EuroPar 2017 Santiago de Compostela, Spain: , Keynote talk, 2017.
[BibTeX] [Download PDF]

@misc{17:autodasp:17,
address = {Santiago de Compostela, Spain},
annote = {http://calvados.di.unipi.it/auto-dasp-17/index.php/program/},
author = {Marco Aldinucci},
date-added = {2021-01-01 19:04:30 +0100},
date-modified = {2021-01-01 19:07:40 +0100},
howpublished = {International Workshop on Autonomic Solutions for Parallel and Distributed Data Stream Processing (Auto-DaSP 2017), Workshop of EuroPar 2017},
keywords = {keynote},
note = {Keynote talk},
month = aug,
title = {Streaming in the {PGAS} Era},
url = {https://datacloud.di.unito.it/index.php/s/H6ZRoK6gLZDQFta},
year = {2017},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2017_AutoDASP_keynote_pgas.pdf}
}

M. Aldinucci, Partitioned Global Address Space in the mainstream of C++ programming, International Parallel Computing (ParCo) Bologna, Italy: , Keynote talk, 2017.
[BibTeX] [Download PDF]

@misc{20:parco:PGAS,
address = {Bologna, Italy},
annote = {http://www.parco.org/keynotes.html},
author = {Marco Aldinucci},
date-added = {2021-01-01 19:00:41 +0100},
date-modified = {2021-01-01 19:04:21 +0100},
howpublished = {International Parallel Computing (ParCo)},
keywords = {keynote},
note = {Keynote talk},
month = sep,
title = {Partitioned Global Address Space in the mainstream of {C++} programming},
url = {https://datacloud.di.unito.it/index.php/s/dTAm7oAfHRTY7BC},
year = {2017},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2017_PARCO_keynote.pdf}
}

M. Aldinucci, Parallel patterns, data-centric concurrency, and heterogeneous computing, 6th IEEE Intl. Conference on High-Performance Computing and Communications (HPCC) Paris, France: , Keynote talk, aug, 2014.
[BibTeX] [Download PDF]

@misc{14:hpcc:datacentric,
address = {Paris, France},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {6th IEEE Intl. Conference on High-Performance Computing and Communications (HPCC)},
keywords = {keynote},
note = {Keynote talk},
month = aug,
title = {Parallel patterns, data-centric concurrency, and heterogeneous computing},
url = {https://datacloud.di.unito.it/index.php/s/mRSTsB6qjKMYiEN},
year = {2014},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2014_HPCC_Aldinucci.pdf}
}

M. Aldinucci, FastFlow: high-level programming patterns with non-blocking lock-free run-time support, Intl. Summer School in Parallel Patterns Dublin, Ireland: , Keynote talk, 2014.
[BibTeX] [Download PDF]

@misc{14:dublin:ff,
address = {Dublin, Ireland},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-02-01 11:32:48 +0100},
howpublished = {Intl. Summer School in Parallel Patterns},
keywords = {keynote},
note = {Keynote talk},
month = jun,
title = {FastFlow: high-level programming patterns with non-blocking lock-free run-time support},
url = {https://datacloud.di.unito.it/index.php/s/TJP46RfwX2NGPTS},
year = {2014},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2014_Dublin_Aldinucci.pdf}
}

M. Aldinucci, Turning Big data into knowledge: Techniques and Tools for Parallel Computing on Online Data Streams in Systems Biology and Epidemiology, Workshop in big data management Rhodes, Greece: , aug, 2012.
[BibTeX] [Download PDF]

@misc{12:bigdata:ff,
address = {Rhodes, Greece},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-02-01 11:21:34 +0100},
howpublished = {Workshop in big data management},
keywords = {keynote},
month = aug,
title = {Turning Big data into knowledge: Techniques and Tools for Parallel Computing on Online Data Streams in Systems Biology and Epidemiology},
url = {https://datacloud.di.unito.it/index.php/s/yg7i63MQdr7gZt7},
year = {2012},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2012_BDMC_Europar_Invited.pdf}
}

Talks related to the ADMIRE, ACROSS, TEXTAROSSA projects

A. Scionti and I. Colonnelli, Orchestrating Multi-Domain Workflows: The ACROSS Approach, Workflows Community: Modern Workflows for Continuum and Cross-Facility Computing Denver, CO, Usa: , November, 2023.
[BibTeX] [Download PDF]

@misc{23:sc:WCIBoF,
address = {Denver, CO, Usa},
author = {Alberto Scionti and Iacopo Colonnelli},
howpublished = {Workflows Community: Modern Workflows for Continuum and Cross-Facility Computing},
keywords = {across, streamflow},
title = {Orchestrating Multi-Domain Workflows: The {ACROSS} Approach},
year = {2023},
month = {November},
url = {https://datacloud.di.unito.it/index.php/s/rJXcDBK4mLmS8yz}
}

I. Colonnelli, ACROSS: HPC Big Data Artificial Intelligence Cross Stack Platform Towards Exascale, LN HPC-KTT Assemblea Nazionale 2023 Pisa, Italy: , October, 2023.
[BibTeX] [Download PDF]

@misc{23:AssembleaHPC-KTT,
address = {Pisa, Italy},
author = {Iacopo Colonnelli},
keywords = {across, streamflow},
month = {October},
howpublished = {LN HPC-KTT Assemblea Nazionale 2023},
title = {{ACROSS}: {HPC} Big Data Artificial Intelligence Cross Stack Platform Towards Exascale},
url = {https://datacloud.di.unito.it/index.php/s/aK7es8BgFeWorjD},
year = {2023}
}

S. Fonio, Benchmarking Federated Learning Frameworks for Medical Imaging Tasks, Image Analysis and Processing – ICIAP 2023 – 22th International Conference – FedMed Udine, Italy: , sep, 2023.
[BibTeX] [Abstract] [Download PDF]

This paper presents a comprehensive benchmarking study of various Federated Learning (FL) frameworks applied to the task of Medical Image Classification. The research specifically addresses the often neglected and complex aspects of scalability and usability in off-the-shelf FL frameworks. Through experimental validation using real case deployments, we provide empirical evidence of the performance and practical relevance of open source FL frameworks. Our findings contribute valuable insights for anyone interested in deploying a FL system, with a particular focus on the healthcare domain—an increasingly attractive field for FL applications.

@misc{23:iciap:benchmed,
address = {Udine, Italy},
author = {Samuele Fonio},
abstract = {This paper presents a comprehensive benchmarking study of various Federated Learning (FL) frameworks applied to the task of Medical Image Classification. The research specifically addresses the often neglected and complex aspects of scalability and usability in off-the-shelf FL frameworks. Through experimental validation using real case deployments, we provide empirical evidence of the performance and practical relevance of open source FL frameworks. Our findings contribute valuable insights for anyone interested in deploying a FL system, with a particular focus on the healthcare domain—an increasingly attractive field for FL applications.},
howpublished = {Image Analysis and Processing - ICIAP 2023 - 22th International Conference - FedMed},
keywords = {icsc, eupilot},
title = {Benchmarking Federated Learning Frameworks for Medical Imaging Tasks},
year = {2023},
month = {sep},
url = {https://datacloud.di.unito.it/index.php/s/sR7YeTGgfH4DtCR}
}

G. Mittone and S. Fonio, Benchmarking Federated Learning Scalability, 2nd Italian Conference on Big Data and Data Science (ITADATA 2023) Naples, Italy: , September, 2023.
[BibTeX] [Abstract] [Download PDF]

Federated Learning (FL) is a widespread Machine Learning paradigm handling distributed Big Data. In this work, we demonstrate that different FL frameworks expose different scaling performances despite adopting the same technologies, highlighting the need for a more comprehensive study on the topic.

@misc{23:itadata:fl_scaling,
address = {Naples, Italy},
author = {Gianluca Mittone and Samuele Fonio},
abstract = {Federated Learning (FL) is a widespread Machine Learning paradigm handling distributed Big Data. In this work, we demonstrate that different FL frameworks expose different scaling performances despite adopting the same technologies, highlighting the need for a more comprehensive study on the topic.},
keywords = {invited, eupilot, icsc},
month = {September},
howpublished = {2nd Italian Conference on Big Data and Data Science (ITADATA 2023)},
title = {Benchmarking Federated Learning Scalability},
note = {},
year = {2023},
url = {https://datacloud.di.unito.it/index.php/s/QZGxC4X3s5LG5oT}
}

G. Mittone, W. Riviera, I. Colonnelli, R. Birke, and M. Aldinucci, Model-Agnostic Federated Learning, 29th International European Conference on Parallel and Distributed Computing (Euro-Par ’23) Limassol, Cyprus: , September, 2023.
[BibTeX] [Abstract] [Download PDF]

Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs); this allowed its development as DNNs proliferated but neglected those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only support DNNs reinforces this problem. To address the lack of non-DNN-based FL solutions, we propose MAFL (Model-Agnostic Federated Learning). MAFL merges a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel® OpenFL. MAFL is the first FL system not tied to any machine learning model, allowing exploration of FL beyond DNNs. We test MAFL from multiple points of view, assessing its correctness, flexibility, and scaling properties up to 64 nodes of an HPC cluster. We also show how we optimised OpenFL achieving a 5.5x speedup over a standard FL scenario. MAFL is compatible with x86-64, ARM-v8, Power and RISC-V.

@misc{23:europar:phdtalk,
address = {Limassol, Cyprus},
author = {Gianluca Mittone and Walter Riviera and Iacopo Colonnelli and Robert Birke and Marco Aldinucci},
abstract = {Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs); this allowed its development as DNNs proliferated but neglected those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only support DNNs reinforces this problem. To address the lack of non-DNN-based FL solutions, we propose MAFL (Model-Agnostic Federated Learning). MAFL merges a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel® OpenFL. MAFL is the first FL system not tied to any machine learning model, allowing exploration of FL beyond DNNs. We test MAFL from multiple points of view, assessing its correctness, flexibility, and scaling properties up to 64 nodes of an HPC cluster. We also show how we optimised OpenFL achieving a 5.5x speedup over a standard FL scenario. MAFL is compatible with x86-64, ARM-v8, Power and RISC-V.},
keywords = {invited, eupilot, icsc},
month = {September},
howpublished = {29th International European Conference on Parallel and Distributed Computing (Euro-Par '23)},
title = {Model-Agnostic Federated Learning},
note = {},
year = {2023},
url = {https://datacloud.di.unito.it/index.php/s/rBxPq7DqNTWxFk8}
}

G. Mittone, R. Birke, and M. Aldinucci, Efficiently Distributed Federated Learning, 29th International European Conference on Parallel and Distributed Computing (Euro-Par ’23) Limassol, Cyprus: , August, 2023.
[BibTeX] [Abstract] [Download PDF]

Federated Learning (FL) is experiencing a substantial research interest, with many frameworks being developed to allow practitioners to build federations easily and quickly. Most of these efforts do not consider two main aspects that are key to Machine Learning (ML) software: customizability and performance. This research addresses these issues by implementing an open-source FL framework named FastFederatedLearning (FFL). FFL is implemented in C/C++, focusing on code performance, and allows the user to specify any communication graph between clients and servers involved in the federation, ensuring customizability. FFL is tested against Intel OpenFL, achieving consistent speedups over different computational platforms (x86-64, ARM-v8, RISC-V), ranging from 2.5x and 3.69x. We aim to wrap FFL with a Python interface to ease its use and implement a middleware for different communication backends to be used. We aim to build dynamic federations in which relations between clients and servers are not static, giving life to an environment where federations can be seen as long-time evolving structures and exploited as services.

@misc{23:europar:phdtalk,
address = {Limassol, Cyprus},
author = {Gianluca Mittone and Robert Birke and Marco Aldinucci},
abstract = {Federated Learning (FL) is experiencing a substantial research interest, with many frameworks being developed to allow practitioners to build federations easily and quickly.
Most of these efforts do not consider two main aspects that are key to Machine Learning (ML) software: customizability and performance.
This research addresses these issues by implementing an open-source FL framework named FastFederatedLearning (FFL).
FFL is implemented in C/C++, focusing on code performance, and allows the user to specify any communication graph between clients and servers involved in the federation, ensuring customizability.
FFL is tested against Intel OpenFL, achieving consistent speedups over different computational platforms (x86-64, ARM-v8, RISC-V), ranging from 2.5x and 3.69x.
We aim to wrap FFL with a Python interface to ease its use and implement a middleware for different communication backends to be used.
We aim to build dynamic federations in which relations between clients and servers are not static, giving life to an environment where federations can be seen as long-time evolving structures and exploited as services.},
keywords = {invited, eupilot, icsc},
month = {August},
howpublished = {29th International European Conference on Parallel and Distributed Computing (Euro-Par '23)},
title = {Efficiently Distributed Federated Learning},
note = {},
year = {2023},
url = {https://datacloud.di.unito.it/index.php/s/pT3qxkwzzsHR3nS}
}

A. Mulone, S. Awad, D. Chiarugi, and M. Aldinucci, Porting the Variant Calling Pipeline for NGS data in cloud-HPC environment, 47th {IEEE} Annual Computers, Software, and Applications Conference, {COMPSAC} 2023 Torino, Italy: , Jun, 2023.
[BibTeX] [Abstract] [Download PDF]

In recent years we have understood the importance of analyzing and sequencing human genetic variation. A relevant aspect that emerged from the Covid-19 pandemic was the need to obtain results very quickly; this involved using High-Performance Computing (HPC) environments to execute the Next Generation Sequencing (NGS) pipeline. However, HPC is not always the most suitable environment for the entire execution of a pipeline, especially when it involves many heterogeneous tools. The ability to execute parts of the pipeline on different environments can lead to higher performance but also cheaper executions. This work shows the design and optimization process that led us to a state-of-the-art Variant Calling hybrid workflow based on the StreamFlow Workflow Management System (WfMS). We also compare StreamFlow with Snakemake, an established WfMS targeting HPC facilities, observing comparable performance on single environments and satisfactory improvements with a hybrid cloud-HPC configuration.

@misc{23:mulone:wide:vcp,
abstract = {In recent years we have understood the importance of analyzing and sequencing human genetic variation. A relevant aspect that emerged from the Covid-19 pandemic was the need to obtain results very quickly; this involved using High-Performance Computing (HPC) environments to execute the Next Generation Sequencing (NGS) pipeline. However, HPC is not always the most suitable environment for the entire execution of a pipeline, especially when it involves many heterogeneous tools. The ability to execute parts of the pipeline on different environments can lead to higher performance but also cheaper executions. This work shows the design and optimization process that led us to a state-of-the-art Variant Calling hybrid workflow based on the StreamFlow Workflow Management System (WfMS). We also compare StreamFlow with Snakemake, an established WfMS targeting HPC facilities, observing comparable performance on single environments and satisfactory improvements with a hybrid cloud-HPC configuration.},
address = {Torino, Italy},
author = {Alberto Mulone and Sherine Awad and Davide Chiarugi and Marco Aldinucci},
howpublished = {47th {IEEE} Annual Computers, Software, and Applications Conference, {COMPSAC} 2023},
keywords = {across, icsc, streamflow},
title = {Porting the {V}ariant {C}alling {P}ipeline for {NGS} data in cloud-{HPC} environment},
year = {2023},
month = {Jun},
url = {https://datacloud.di.unito.it/index.php/s/zNLj3LCZNsNxHwy}
}

G. Mittone, F. Svoboda, M. Aldinucci, N. D. Lane, and P. Lio’, A Federated Learning Benchmark for Drug-Target Interaction, 2023 ACM international Web Conference (WWW ’23) , Invited talk, May, 2023.
[BibTeX] [Abstract] [Download PDF]

Aggregating pharmaceutical data in the drug-target interaction (DTI) domain can potentially deliver life-saving breakthroughs. It is, however, notoriously difficult due to regulatory constraints and commercial interests. This work proposes the application of federated learning, which is reconcilable with the industry’s constraints. It does not require sharing any information that would reveal the entities’ data or any other high-level summary. When used on a representative GraphDTA model and the KIBA dataset, it achieves up to 15\% improved performance relative to the best available non-privacy preserving alternative. Our extensive battery of experiments shows that, unlike in other domains, the non-IID data distribution in the DTI datasets does not deteriorate FL performance. Additionally, we identify a material trade-off between the benefits of adding new data and the cost of adding more clients.

@misc{23:WWW,
abstract = {Aggregating pharmaceutical data in the drug-target interaction (DTI) domain can potentially deliver life-saving breakthroughs. It is, however, notoriously difficult due to regulatory constraints and commercial interests. This work proposes the application of federated learning, which is reconcilable with the industry's constraints. It does not require sharing any information that would reveal the entities' data or any other high-level summary. When used on a representative GraphDTA model and the KIBA dataset, it achieves up to 15\% improved performance relative to the best available non-privacy preserving alternative. Our extensive battery of experiments shows that, unlike in other domains, the non-IID data distribution in the DTI datasets does not deteriorate FL performance. Additionally, we identify a material trade-off between the benefits of adding new data and the cost of adding more clients.},
author = {Gianluca Mittone and Filip Svoboda and Marco Aldinucci and Nicholas D. Lane and Pietro Lio'},
howpublished = {2023 ACM international Web Conference (WWW '23)},
keywords = {invited, eupilot, icsc},
month = {May},
note = {Invited talk},
title = {A Federated Learning Benchmark for Drug-Target Interaction},
url = {https://datacloud.di.unito.it/index.php/s/js7go3EorZxSLn9},
year = {2023}
}

G. Mittone, N. Tonci, R. Birke, I. Colonnelli, D. Medić, A. Bartolini, R. Esposito, E. Parisi, F. Beneventi, M. Polato, M. Torquati, L. Benini, and M. Aldinucci, Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning, 20th ACM international conference on computing frontiers (CF ’23) , Invited talk, May, 2023.
[BibTeX] [Abstract] [Download PDF]

Decentralised Machine Learning (DML) enables collaborative machine learning without centralised input data. Federated Learning (FL) and Edge Inference are examples of DML. While tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel processors (e.g., RISC-V), non-fully connected network topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing us to map DML schemes to an underlying middleware, i.e. the FastFlow parallel programming library. We experiment with it by generating different working DML schemes on x86-64 and ARM platforms and an emerging RISC-V one. We characterise the performance and energy efficiency of the presented schemes and systems. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge.

@misc{23:ACMCF,
abstract = {Decentralised Machine Learning (DML) enables collaborative machine learning without centralised input data. Federated Learning (FL) and Edge Inference are examples of DML. While tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel processors (e.g., RISC-V), non-fully connected network topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing us to map DML schemes to an underlying middleware, i.e. the FastFlow parallel programming library. We experiment with it by generating different working DML schemes on x86-64 and ARM platforms and an emerging RISC-V one. We characterise the performance and energy efficiency of the presented schemes and systems. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge.},
author = {Gianluca Mittone and Nicolò Tonci and Robert Birke and Iacopo Colonnelli and Doriana Medić and Andrea Bartolini and Roberto Esposito and Emanuele Parisi and Francesco Beneventi and Mirko Polato and Massimo Torquati and Luca Benini and Marco Aldinucci},
howpublished = {20th ACM international conference on computing frontiers (CF '23)},
keywords = {invited, eupilot, icsc},
month = {May},
note = {Invited talk},
title = {Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning},
url = {https://datacloud.di.unito.it/index.php/s/BYyqZbHzzN4DL8Z},
year = {2023}
}

S. Karvounari, E. Mathioulaki, M. R. Crusoe, and I. Colonnelli, Standardised Workflows at EBRAINS, Human Brain Project Summit 2023 Marseille, France: , Invited talk, March, 2023.
[BibTeX] [Abstract] [Download PDF]

A hands-on training offer for Standardised Workflows in EBRAINS. A short presentation will be used as an introduction, while the main hands-on session will provide information about Writing and Executing Standardised Workflows. TC will give some guidelines, so attendees can experiment with writing CWL tools and workflows and then they will be given access to VM to execute these workflows. The Workflows Dashboard will be also presented during the same session, offering to the attendees the opportunity to understand the different functionalities, use it with TC support and provide useful comments.

@misc{23:HBPSummit,
abstract = {A hands-on training offer for Standardised Workflows in EBRAINS. A short presentation will be used as an introduction, while the main hands-on session will provide information about Writing and Executing Standardised Workflows. TC will give some guidelines, so attendees can experiment with writing CWL tools and workflows and then they will be given access to VM to execute these workflows. The Workflows Dashboard will be also presented during the same session, offering to the attendees the opportunity to understand the different functionalities, use it with TC support and provide useful comments.},
address = {Marseille, France},
author = {Sofia Karvounari and Eleni Mathioulaki and Michael R. Crusoe and Iacopo Colonnelli},
howpublished = {Human Brain Project Summit 2023},
keywords = {invited, streamflow, across, eupex, space},
month = {March},
note = {Invited talk},
title = {Standardised Workflows at {EBRAINS}},
url = {https://datacloud.di.unito.it/index.php/s/K5YQKTsX9N7NLT8},
year = {2023}
}

M. Aldinucci, Experimenting with Systems for Decentralized Machine Learning, NVidia GTC 2023 , March, 2023.
[BibTeX] [Abstract] [Download PDF]

Decentralized machine learning (DML) enables collaborative machine learning without centralized input data. Federated learning (FL) and edge inference (EI) are examples of DML. Collaboration naturally happens at the edge of a distributed system with inherently distributed data. While tools for DML are starting to flourish, much needs to be done to get more flexible and portable tools to experiment with novel techniques, non-fully connected topologies, multiple data domains, and asynchronous collaboration schemes. We’ll present recent advances in DML, aiming to improve usability in data centers and, at the edge, to widen the class of models extending FL to non-DNN paradigms, to improve the accuracy of models controlling normalization and frequency of communications, and to boost data privacy though generative adversarial networks. Prerequisites: Intermediate understanding of machine learning methods and distributed & parallel computing.

@misc{23:gtc:fl,
optkey = {},
author = {Marco Aldinucci},
title = {Experimenting with Systems for Decentralized Machine Learning},
howpublished = {NVidia GTC 2023},
month = {March},
year = {2023},
optnote = {},
optannote = {},
keywords = {hpc4ai, eupex, across, textarossa, admire, eupilot, epi, space, eumaster4hpc},
abstract = {Decentralized machine learning (DML) enables collaborative machine learning without centralized input data. Federated learning (FL) and edge inference (EI) are examples of DML. Collaboration naturally happens at the edge of a distributed system with inherently distributed data. While tools for DML are starting to flourish, much needs to be done to get more flexible and portable tools to experiment with novel techniques, non-fully connected topologies, multiple data domains, and asynchronous collaboration schemes. We'll present recent advances in DML, aiming to improve usability in data centers and, at the edge, to widen the class of models extending FL to non-DNN paradigms, to improve the accuracy of models controlling normalization and frequency of communications, and to boost data privacy though generative adversarial networks. Prerequisites: Intermediate understanding of machine learning methods and distributed & parallel computing.},
url = {https://datacloud.di.unito.it/index.php/s/oyLt7xwkbKxz65c}
}

M. Aldinucci, HPC4AI: The Research on AI beyond the public cloud, CENTAI kick-off meeting Torino, Italy: , March, 2023.
[BibTeX] [Download PDF]

@misc{23:CENTAI:hpc4ai,
optkey = {},
author = {Marco Aldinucci},
title = {{HPC4AI}: The Research on {AI} beyond the public cloud},
howpublished = {CENTAI kick-off meeting},
month = {March},
year = {2023},
address = {Torino, Italy},
optnote = {},
optannote = {},
keywords = {invited, hpc4ai, eupex, across, textarossa, admire, eupilot, epi, eumaster4hpc, space, brainteaser},
abstract = {},
url = {https://datacloud.di.unito.it/index.php/s/PZXjPm8sfKTmTGb}
}

I. Colonnelli, CWL for HPC: are we there yet?, 2023 CWL Conference Heidelberg, Germany: , Invited talk, March, 2023.
[BibTeX] [Abstract] [Download PDF]

Modern HPC applications are becoming so heterogeneous and complex that a modular approach to their design, deployment and orchestration is now necessary. This talk explores the benefits of using a vendor-agnostic workflow language (CWL) coupled with a hybrid workflow management system (StreamFlow) in the HPC ecosystem. Also, it will examine the requirements needed to model HPC applications effectively, the CWL’s readiness to meet such requirements, and the proposals made to improve the language where needed. Four real use cases will drive the discussion: the ACROSS Project (G.A. n. 955648), where CWL is the primary interface to model three HPC workflows, and the EUPEX Project (G.A. n. 101033975), where StreamFlow will be used for the rapid prototyping of a seismic engineering HPC application for a Modular Supercomputing Architecture (MSA) system.

@misc{23:CWLConference,
abstract = {Modern HPC applications are becoming so heterogeneous and complex that a modular approach to their design, deployment and orchestration is now necessary. This talk explores the benefits of using a vendor-agnostic workflow language (CWL) coupled with a hybrid workflow management system (StreamFlow) in the HPC ecosystem. Also, it will examine the requirements needed to model HPC applications effectively, the CWL’s readiness to meet such requirements, and the proposals made to improve the language where needed. Four real use cases will drive the discussion: the ACROSS Project (G.A. n. 955648), where CWL is the primary interface to model three HPC workflows, and the EUPEX Project (G.A. n. 101033975), where StreamFlow will be used for the rapid prototyping of a seismic engineering HPC application for a Modular Supercomputing Architecture (MSA) system.},
address = {Heidelberg, Germany},
author = {Iacopo Colonnelli},
howpublished = {2023 CWL Conference},
keywords = {invited, streamflow, across, eupex},
month = {March},
note = {Invited talk},
title = {{CWL} for {HPC}: are we there yet?},
url = {https://datacloud.di.unito.it/index.php/s/CMCd5LiZeXsxwEg},
year = {2023}
}

M. Aldinucci, From HPC4AI to ICSC living lab: Where systems are the research, Dell Advanced Computing Workshop 2023: HPC and Beyond Bologna, Italy: , Feb, 2023.
[BibTeX] [Download PDF]

@misc{23:Dell:hpc4ai,
optkey = {},
author = {Marco Aldinucci},
title = {From {HPC4AI} to {ICSC} living lab: Where systems are the research},
howpublished = {Dell Advanced Computing Workshop 2023: HPC and Beyond},
month = {Feb},
year = {2023},
address = {Bologna, Italy},
optnote = {},
optannote = {},
keywords = {invited, hpc4ai, futurehpc, eupex, textarossa, admire, eupilot},
abstract = {},
url = {https://datacloud.di.unito.it/index.php/s/M5QRJyDxyxokcfL}
}

G. Mittone, Paving the way to innovative tools for Federated Learning, 2023 HiPEAC Conference Toulouse, France: , Invited talk, February, 2023.
[BibTeX] [Abstract] [Download PDF]

Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs). On the one hand, this allowed its development and widespread use as DNNs proliferated. On the other hand, it neglected all those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only allow training DNNs reinforces this problem. To address the lack of FL solutions for non-DNN-based use cases, we propose MAFL (Model-Agnostic Federated Learning). MAFL marries a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel® OpenFL. MAFL is the first FL system not tied to any specific type of machine learning model, allowing exploration of FL scenarios beyond DNNs and trees. Furthermore, tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel systems (e.g., RISC-V), non-fully connected topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing to map DML schemes to an underlying middleware, i.e. the \ff parallel programming library. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge.

@misc{23:hipeac,
abstract = {Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs). On the one hand, this allowed its development and widespread use as DNNs proliferated. On the other hand, it neglected all those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only allow training DNNs reinforces this problem. To address the lack of FL solutions for non-DNN-based use cases, we propose MAFL (Model-Agnostic Federated Learning). MAFL marries a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel® OpenFL. MAFL is the first FL system not tied to any specific type of machine learning model, allowing exploration of FL scenarios beyond DNNs and trees. Furthermore, tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel systems (e.g., RISC-V), non-fully connected topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing to map DML schemes to an underlying middleware, i.e. the \ff parallel programming library. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge.},
address = {Toulouse, France},
author = {Gianluca Mittone},
howpublished = {2023 HiPEAC Conference},
keywords = {invited, eupilot},
month = {February},
note = {Invited talk},
title = {Paving the way to innovative tools for Federated Learning},
url = {https://datacloud.di.unito.it/index.php/s/2GtxPidHq79RTzA},
year = {2023}
}

D. Medić and M. Aldinucci, Towards formal model for location aware workflows Torino, Italy: , 2023.
[BibTeX] [Abstract] [Download PDF]

Designing complex applications and executing them on large-scale topologies of heterogeneous architectures is becoming increasingly crucial in many scientific domains. As a result, diverse workflow modelling paradigms are developed, most of them with no formalisation provided. In these circumstances, comparing two different models or switching from one system to the other becomes a hard nut to crack. This paper investigates the capability of process algebra to model a location aware workflow system. Distributed $\pi$-calculus is considered as the base of the formal model due to its ability to describe the communicating components that change their structure as an outcome of the communication. Later, it is discussed how the base model could be extended or modified to capture different features of location aware workflow system. The intention of this paper is to highlight the fact that due to its flexibility, $\pi$-calculus, could be a good candidate to represent the behavioural perspective of the workflow system.

@misc{23:wide:medic,
address = {Torino, Italy},
author = {Doriana Medi\'{c} and Marco Aldinucci},
abstract = {Designing complex applications and executing them on large-scale topologies of heterogeneous architectures is becoming increasingly crucial in many scientific domains. As a result, diverse workflow modelling paradigms are developed, most of them with no formalisation provided. In these circumstances, comparing two different models or switching from one system to the other becomes a hard nut to crack.
This paper investigates the capability of process algebra to model a location aware workflow system. Distributed $\pi$-calculus is considered as the base of the formal model due to its ability to describe the communicating components that change their structure as an outcome of the communication. Later, it is discussed how the base model could be extended or modified to capture different features of location aware workflow system.
The intention of this paper is to highlight the fact that due to its flexibility, $\pi$-calculus, could be a good candidate to represent the behavioural perspective of the workflow system.},
keywords = {eupex, icsc},
title = {Towards formal model for location aware workflows},
year = {2023},
url = {https://datacloud.di.unito.it/index.php/s/SgTbE2Ltbg38KtS}
}

I. Colonnelli and M. Aldinucci, Hybrid Workflows For Large-Scale Scientific Applications, 6th EAGE High Performance Computing Workshop Milano, Italy: , Sep, 2022.
[BibTeX] [Download PDF]

@misc{22:eage,
abstract = {Large-scale scientific applications are facing an irreversible transition from monolithic, high-performance oriented codes to modular and polyglot deployments of specialised (micro-)services. The reasons behind this transition are many: coupling of standard solvers with Deep Learning techniques, offloading of data analysis and visualisation to Cloud, and the advent of specialised hardware accelerators. Topology-aware Workflow Management Systems (WMSs) play a crucial role. In particular, topology-awareness allows an explicit mapping of workflow steps onto heterogeneous locations, allowing automated executions on top of hybrid architectures (e.g., cloud+HPC or classical+quantum). Plus, topology-aware WMSs can offer non-functional requirements OOTB, e.g. components’ life-cycle orchestration, secure and efficient data transfers, fault tolerance, and cross-cluster execution of urgent workloads. Augmenting interactive Jupyter Notebooks with distributed workflow capabilities allows domain experts to prototype and scale applications using the same technological stack, while relying on a feature-rich and user-friendly web interface. This abstract will showcase how these general methodologies can be applied to a typical geoscience simulation pipeline based on the Full Wavefront Inversion (FWI) technique. In particular, a prototypical Jupyter Notebook will be executed interactively on Cloud. Preliminary data analyses and post-processing will be executed locally, while the computationally demanding optimisation loop will be scheduled on a remote HPC cluster.},
author = {Iacopo Colonnelli and Marco Aldinucci},
title = {Hybrid Workflows For Large-Scale Scientific Applications},
howpublished = {6th EAGE High Performance Computing Workshop},
month = {Sep},
year = {2022},
note = {},
address = {Milano, Italy},
keywords = {eupex, across, textarossa, jupyter-workflow},
abstract = {},
annote = {},
url = {https://datacloud.di.unito.it/index.php/s/GScPS5LCPdt6Yoo}
}

I. Colonnelli, B. Cantalupo, D. Medić, and M. Aldinucci, Hybrid workflows for heterogeneous distributed computing, 3rd Italian Workshop on HPC (ITWSHPC) Torino, Italy: , Sep, 2022.
[BibTeX] [Download PDF]

@misc{22:itwshpc,
author = {Iacopo Colonnelli and Barbara Cantalupo and Doriana Medi\'{c} and Marco Aldinucci},
title = {Hybrid workflows for heterogeneous distributed computing},
howpublished = {3rd Italian Workshop on HPC (ITWSHPC)},
month = {Sep},
year = {2022},
note = {},
address = {Torino, Italy},
keywords = {eupex, across, admire, eupilot, textarossa, eumaster4hpc},
abstract = {},
annote = {},
url = {https://datacloud.di.unito.it/index.php/s/ienbcA2DJ26aioE}
}

I. Colonnelli and M. Aldinucci, CINI HPC-KTT: HPC Key Technologies and Tools National Lab, NVIDIA HPC Roundtable Casalecchio di Reno, Italy: , Invited talk, Sep, 2022.
[BibTeX] [Download PDF]

@misc{22:nvidia_hpc_roundtable,
author = {Iacopo Colonnelli and Marco Aldinucci},
title = {{CINI HPC-KTT}: {HPC} {K}ey {T}echnologies and {T}ools National Lab},
howpublished = {NVIDIA HPC Roundtable},
month = {Sep},
year = {2022},
note = {Invited talk},
address = {Casalecchio di Reno, Italy},
keywords = {invited, eupex, across, admire, eupilot, textarossa, eumaster4hpc},
abstract = {},
annote = {},
url = {https://datacloud.di.unito.it/index.php/s/9EQniZ2dGzdJ26f}
}

M. Aldinucci, La convergenza HPC-cloud è l’anello mancante tra il calcolo scientifico e l’IA applicata, Intelligenza Artificiale e Business Applications Virtual event: , Invited talk, Sep, 2022.
[BibTeX] [Download PDF]

@misc{22:soiel:ai,
abstract = {Innanzitutto, le infrastrutture HPC stanno adottando le GPU per il loro rapporto prestazioni per watt superiore rispetto ai multicore generici. In secondo luogo, i flussi di lavoro scientifici di prossima generazione stanno integrando passaggi basati sull'intelligenza artificiale per la loro precisione nell'approssimazione e nell'analisi di fenomeni complessi. In terzo luogo, l'IA e in particolare il Machine Learning (ML) rappresentano un carico di lavoro perfetto per le GPU in termini di prestazioni e tempo di sviluppo. Oggi non possiamo ancora chiudere il cerchio eseguendo senza problemi carichi di lavoro scientifici abilitati all'intelligenza artificiale nelle infrastrutture HPC perché il loro software di sistema e gli strumenti di sviluppo non sono progettati per i carichi di lavoro moderni, come i framework ML progettati per il cloud. È probabile che la convergenza HPC-cloud colmi il divario.
Nel talk verranno presentate le infrastrutture e gli strumenti sviluppati all'Università di Torino per la convergenza HPC-cloud (es. HPC4AI, StreamFlow, CAPIO, Jupyter-workflow) e come sono stati utilizzati per le applicazioni di intelligenza artificiale, come la diagnosi spiegabile di polmonite COVID-19 e la tutela della privacy AI. L'esperienza maturata nella progettazione e gestione di HPC4AI costituisce il cuore della progettazione del laboratorio di contaminazione del "FutureHPC" di Torino secondo il Centro Nazionale "HPC, BigData e Quantum Computing" finanziato dal PNRR con 320M€ che dovrebbe essere operativo dal 1 settembre 2022. L'obiettivo finale del laboratorio di contaminazione è sviluppare relazioni e collaborazioni tra industria e università.},
author = {Marco Aldinucci},
title = {La convergenza HPC-cloud è l'anello mancante tra il calcolo scientifico e l'IA applicata},
howpublished = {Intelligenza Artificiale e Business Applications},
month = {Sep},
year = {2022},
note = {Invited talk},
address = {Virtual event},
keywords = {invited, eupex, across, textarossa, admire, eupilot, eumaster4hpc},
abstract = {},
annote = {},
url = {https://datacloud.di.unito.it/index.php/s/xCQSqJ8bCKCXMK9}
}

B. Casella, Benchmarking FedAvg and FedCurv for Image Classification Tasks, ITADATA Milan, Italy: , Sep, 2022.
[BibTeX] [Abstract] [Download PDF]

Presentation of the paper “Benchmarking FedAvg and FedCurv for Image Classification Tasks” to the first italian conference on Big Data and Data Science

@misc{22:itadata,
abstract = {Presentation of the paper "Benchmarking FedAvg and FedCurv for Image Classification Tasks" to the first italian conference on Big Data and Data Science},
author = {Bruno Casella},
title = {Benchmarking FedAvg and FedCurv for Image Classification Tasks
},
howpublished = {ITADATA},
month = {Sep},
year = {2022},
address = {Milan, Italy},
keywords = {eupilot},
annote = {},
url = {https://datacloud.di.unito.it/index.php/s/6XaEXnAowRrAHGL}
}

M. Aldinucci, Il calcolo parallelo: una storia di metodi e algoritmi raccontata dalle macchine, Olimpiadi di Informatica Biella, Italy: , Invited talk, Sep, 2022.
[BibTeX] [Download PDF]

@misc{22:olimpiadi:cs,
abstract = {Lectio Magistralis alle finali nazionali delle Olimpiadi di Informatica 2022},
author = {Marco Aldinucci},
title = {Il calcolo parallelo: una storia di metodi e algoritmi raccontata dalle macchine
},
howpublished = {Olimpiadi di Informatica},
month = {Sep},
year = {2022},
note = {Invited talk},
address = {Biella, Italy},
keywords = {invited, eupex, across, textarossa, admire, eupilot, eumaster4hpc},
abstract = {},
annote = {},
url = {https://datacloud.di.unito.it/index.php/s/7ZdfLkn3NetzXCN}
}

I. Colonnelli and D. Tranchitella, Dossier: multi-tenant distributed Jupyter Notebooks, DoK Talks 141 Virtual event: , Invited talk, July, 2022.
[BibTeX] [Abstract] [Download PDF]

When providing data analysis as a service, one must tackle several problems. Data privacy and protection by design are crucial when working on sensitive data. Performance and scalability are fundamental for compute-intensive workloads, e.g. training Deep Neural Networks. User-friendly interfaces and fast prototyping tools are essential to allow domain experts to experiment with new techniques. Portability and reproducibility are necessary to assess the actual value of results. Kubernetes is the best platform to provide reliable, elastic, and maintainable services. However, Kubernetes alone is not enough to achieve large-scale multi-tenant reproducible data analysis. OOTB support for multi-tenancy is too rough, with only two levels of segregation (i.e. the single namespace or the entire cluster). Offloading computation to off-cluster resources is non-trivial and requires the user’s manual configuration. Also, Jupyter Notebooks per se cannot provide much scalability (they execute locally and sequentially) and reproducibility (users can run cells in any order and any number of times). The Dossier platform allows system administrators to manage multi-tenant distributed Jupyter Notebooks at the cluster level in the Kubernetes way, i.e. through CRDs. Namespaces are aggregated in Tenants, and all security and accountability aspects are managed at that level. Each Notebook spawns into a user-dedicated namespace, subject to all Tenant-level constraints. Users can rely on provisioned resources, either in-cluster worker nodes or external resources like HPC facilities. Plus, they can plug their computing nodes in a BYOD fashion. Notebooks are interpreted as distributed workflows, where each cell is a task that one can offload to a different location in charge of its execution.

@misc{22:data-on-kubernetes,
optkey = {},
author = {Iacopo Colonnelli and Dario Tranchitella},
abstract = {When providing data analysis as a service, one must tackle several problems. Data privacy and protection by design are crucial when working on sensitive data. Performance and scalability are fundamental for compute-intensive workloads, e.g. training Deep Neural Networks. User-friendly interfaces and fast prototyping tools are essential to allow domain experts to experiment with new techniques. Portability and reproducibility are necessary to assess the actual value of results. Kubernetes is the best platform to provide reliable, elastic, and maintainable services. However, Kubernetes alone is not enough to achieve large-scale multi-tenant reproducible data analysis. OOTB support for multi-tenancy is too rough, with only two levels of segregation (i.e. the single namespace or the entire cluster). Offloading computation to off-cluster resources is non-trivial and requires the user's manual configuration. Also, Jupyter Notebooks per se cannot provide much scalability (they execute locally and sequentially) and reproducibility (users can run cells in any order and any number of times). The Dossier platform allows system administrators to manage multi-tenant distributed Jupyter Notebooks at the cluster level in the Kubernetes way, i.e. through CRDs. Namespaces are aggregated in Tenants, and all security and accountability aspects are managed at that level. Each Notebook spawns into a user-dedicated namespace, subject to all Tenant-level constraints. Users can rely on provisioned resources, either in-cluster worker nodes or external resources like HPC facilities. Plus, they can plug their computing nodes in a BYOD fashion. Notebooks are interpreted as distributed workflows, where each cell is a task that one can offload to a different location in charge of its execution.},
title = {Dossier: multi-tenant distributed {J}upyter {N}otebooks},
howpublished = {DoK Talks 141},
month = {July},
year = {2022},
keywords = {jupyter-workflow, across, deephealth, hpc4ai},
note = {Invited talk},
address = {Virtual event},
optannote = {},
url = {https://datacloud.di.unito.it/index.php/s/RNqTGmTqWS66qHT}
}

I. Colonnelli, StreamFlow, 2nd HealthyCloud Workshop: Analysis of existing orchestration mechanisms for distributed computational analyses Virtual event: , Invited talk, July, 2022.
[BibTeX] [Download PDF]

@misc{22:healthycloud-workshop,
optkey = {},
author = {Iacopo Colonnelli},
title = {{StreamFlow}},
howpublished = {2nd HealthyCloud Workshop: Analysis of existing orchestration mechanisms for distributed computational analyses},
month = {July},
year = {2022},
keywords = {invited, streamflow, deephealth, across, eupex, textarossa},
note = {Invited talk},
address = {Virtual event},
optannote = {},
url = {https://datacloud.di.unito.it/index.php/s/Taz8qtzmkmn9ffT}
}

I. Colonnelli, StreamFlow: a topology-aware WMS, ELIXIR Cloud, Data & AAI Bi-weekly Technical Calls Virtual event: , Invited talk, June, 2022.
[BibTeX] [Download PDF]

@misc{22:elixir-streamflow,
optkey = {},
author = {Iacopo Colonnelli},
title = {{StreamFlow}: a topology-aware {WMS}},
howpublished = {ELIXIR Cloud, Data & AAI Bi-weekly Technical Calls},
month = {June},
year = {2022},
keywords = {invited, streamflow, dephealth, across, eupex, textarossa},
note = {Invited talk},
address = {Virtual event},
optannote = {},
url = {https://datacloud.di.unito.it/index.php/s/Z9GsKnRCxmBdMd3}
}

First, HPC infrastructures are embracing GPUs for their superior performance-per-watt ratio against general-purpose multicores. Second, the next-generation scientific workflows are integrating AI-based steps for their accuracy in approximating and analyzing complex phenomena. Third, AI and specifically Machine Learning (ML), is a perfect workload for GPUs in terms of performance and development time. Today, we cannot still close the circle seamlessly running AI-enabled scientific workloads into HPC infrastructures because their system software and development tools are not designed for modern workloads, such as ML frameworks designed for the cloud. HPC-cloud convergence is likely to bridge the gap. In the talk, we will present Streamflow and CAPIO, two development tools for HPC-cloud convergence.

@misc{22:ml4astro,
address = {Catania, Italy},
author = {Marco Aldinucci},
howpublished = {Machine Learning for Astrophysics {(ML4ASTRO)}},
keywords = {keynote, deephealth, eupex, across, eupilot},
month = {June},
title = {{HPC}-cloud convergence is the missing link between scientific computing and applied{-AI}},
note = {Keynote talk},
year = {2022},
url = {https://datacloud.di.unito.it/index.php/s/2SGswkcip7MoMoH},
abstract = {First, HPC infrastructures are embracing GPUs for their superior performance-per-watt ratio against general-purpose multicores. Second, the next-generation scientific workflows are integrating AI-based steps for their accuracy in approximating and analyzing complex phenomena. Third, AI and specifically Machine Learning (ML), is a perfect workload for GPUs in terms of performance and development time. Today, we cannot still close the circle seamlessly running AI-enabled scientific workloads into HPC infrastructures because their system software and development tools are not designed for modern workloads, such as ML frameworks designed for the cloud. HPC-cloud convergence is likely to bridge the gap. In the talk, we will present Streamflow and CAPIO, two development tools for HPC-cloud convergence.}
}

M. Aldinucci, From small files to no files, 6th Workshop on Performance and Scalability of Storage Systems Paris, France: , Invited talk, June, 2022.
[BibTeX] [Abstract] [Download PDF]

Modern distributed high-performance storage systems saturate the network bandwidth, and the margins for improvement at the software level are tiny. Due to metadata access, they might be troubled with massive access to small files. An example is the Software Heritage (SH) dataset, half petabytes of files with an average size of 3kBytes (Terabytes of metadata). While working with SH, we developed the idea of substituting files with in-memory streams. We did it living in dread with the fear of asking application programmers to rewrite their lovely antique legacy code exploiting the POSIX interface, and up to now, we did not. In the talk, we will introduce CAPIO (Cross-Application Programmable I/O) design principles and the current state of development of the prototype.

@misc{22:p3s:capio,
author = {Marco Aldinucci},
title = {From small files to no files},
howpublished = {6th Workshop on Performance and Scalability of Storage Systems},
month = {June},
year = {2022},
note = {Invited talk},
address = {Paris, France},
keywords = {invited, admire, eupex},
abstract = {Modern distributed high-performance storage systems saturate the network bandwidth, and the margins for improvement at the software level are tiny. Due to metadata access, they might be troubled with massive access to small files. An example is the Software Heritage (SH) dataset, half petabytes of files with an average size of 3kBytes (Terabytes of metadata). While working with SH, we developed the idea of substituting files with in-memory streams. We did it living in dread with the fear of asking application programmers to rewrite their lovely antique legacy code exploiting the POSIX interface, and up to now, we did not. In the talk, we will introduce CAPIO (Cross-Application Programmable I/O) design principles and the current state of development of the prototype.},
annote = {https://per3s.github.io},
url = {https://datacloud.di.unito.it/index.php/s/KLDi87xQmX86iXg}
}

I. Colonnelli and M. Aldinucci, T4.1: Streaming models, TEXTAROSSA General Meeting Roma, Italy: , June, 2022.
[BibTeX] [Download PDF]

@misc{22:textarossa-ga-meeting,
optkey = {},
author = {Iacopo Colonnelli and Marco Aldinucci},
title = {T4.1: Streaming models},
howpublished = {TEXTAROSSA General Meeting},
month = {June},
year = {2022},
keywords = {textarossa},
address = {Roma, Italy},
optannote = {},
url = {https://datacloud.di.unito.it/index.php/s/cNBnwSnTc8GiCkN}
}

M. Aldinucci, The Italian HPC ecosystem and the next generation of EuroHPC CoE, EuroHPC EoCoE final summit Napoli, Italy: , Invited talk, June, 2022.
[BibTeX] [Download PDF]

@misc{22:eocoe:summit,
abstract = {The talk presents the main investments currently ongoing in Italy in the HPC area as well as the activity of Italian stakeholders within EuroHPC. The novel Italian National Centre on HPC (ICSC) is introduced.},
author = {Marco Aldinucci},
title = {The Italian HPC ecosystem and the next generation of EuroHPC CoE},
howpublished = {EuroHPC EoCoE final summit},
month = {June},
year = {2022},
note = {Invited talk},
address = {Napoli, Italy},
keywords = {invited, admire, eupex, across, eupilot, textarossa, eumaster4hpc, icsc},
abstract = {},
annote = {},
url = {https://datacloud.di.unito.it/index.php/s/AH5Ms3NekeoEooB}
}

M. Aldinucci, EuroHPC and the Italian HPC ecosystem, Critical Infrastructure Protection Forum – EuroCC Romania Bucharest, Romania: , Invited talk, June, 2022.
[BibTeX] [Download PDF]

@misc{22:cip:romania,
abstract = {The talk presents the main investments currently ongoing in Italy in the HPC area as well as the activity of Italian stakeholders within EuroHPC. The novel Italian National Centre on HPC (ICSC) is introduced.},
author = {Marco Aldinucci},
title = {EuroHPC and the Italian HPC ecosystem},
howpublished = {Critical Infrastructure Protection Forum - EuroCC Romania},
month = {June},
year = {2022},
note = {Invited talk},
address = {Bucharest, Romania},
keywords = {invited, admire, eupex, across, eupilot, textarossa, eumaster4hpc, icsc},
abstract = {},
annote = {},
url = {https://datacloud.di.unito.it/index.php/s/5dFFoNsZzwTzQkn}
}

M. Aldinucci, Cognitive continuum: a game theoretical approach, HiPEAC Vision meeting, Brussels, 16 May 2022 Brussels, Belgium: , May, 2022.
[BibTeX] [Download PDF]

@misc{22:hipeacvision:fl,
abstract = {Cognitive continuum: a game theoretical approach, (maybe) data operations are too basic: read, write, copy, remove … The talk is aimed to contribute to the forthcoming HiPEAC Vision document},
author = {Marco Aldinucci},
title = {Cognitive continuum: a game theoretical approach},
howpublished = {HiPEAC Vision meeting, Brussels, 16 May 2022},
month = {May},
year = {2022},
note = {},
address = {Brussels, Belgium},
keywords = {invited, admire, eupex, across, eupilot, textarossa, eumaster4hpc, brainteaser},
abstract = {},
annote = {},
url = {https://datacloud.di.unito.it/index.php/s/453HWfmrQyo7j9E}
}

M. Aldinucci, Da HPC4AI al living lab dello spoke FutureHPC del Centro Nazionale HPC, Condivisioni, Conferenza GARR 2022 Palermo, Italy: , Keynote talk, May, 2022.
[BibTeX] [Abstract] [Download PDF]

HPC4AI is an open-access laboratory of the University of Turin open to researchers, students and companies that manages a double pair of systems: a production cloud-HPC system and its twin dedicated to development. The cloud-HPC system is implemented thanks to an extended version of the GARR cloud (OpenStack) and the SLURM workload manager. HPC4AI is specifically designed to support system software development and cloud-HPC convergence tools. Among these streamflow (WMS), jupyter-as-a-service (SaaS), portable-secure-tenant (PasS). The experience gained in the design and management of HPC4AI forms the heart of the design of the livinglab of the Turin “FutureHPC” spoke of the National Center “HPC, BigData and Quantum Computing” funded by the PNRR which should be operational from September 2022.

@misc{22:garr,
optkey = {},
author = {Marco Aldinucci},
title = {Da {HPC4AI} al living lab dello spoke {FutureHPC} del Centro Nazionale {HPC}},
howpublished = {Condivisioni, Conferenza GARR 2022},
month = {May},
year = {2022},
keywords = {keynote, hpc4ai, across, eupex, across, admire, textarossa, eumaster4hpc, icsc},
address = {Palermo, Italy},
note = {Keynote talk},
optannote = {},
abstract = {HPC4AI is an open-access laboratory of the University of Turin open to researchers, students and companies that manages a double pair of systems: a production cloud-HPC system and its twin dedicated to development. The cloud-HPC system is implemented thanks to an extended version of the GARR cloud (OpenStack) and the SLURM workload manager. HPC4AI is specifically designed to support system software development and cloud-HPC convergence tools. Among these streamflow (WMS), jupyter-as-a-service (SaaS), portable-secure-tenant (PasS). The experience gained in the design and management of HPC4AI forms the heart of the design of the livinglab of the Turin "FutureHPC" spoke of the National Center "HPC, BigData and Quantum Computing" funded by the PNRR which should be operational from September 2022.},
url = {https://datacloud.di.unito.it/index.php/s/P3KSroSSmrRxZMc}
}

I. Colonnelli and D. Tranchitella, OpenDeepHealth: Crafting a Deep Learning Platform as a Service with Kubernetes, J on The Beach 2022 Malaga, Spain: , April, 2022.
[BibTeX] [Download PDF]

@misc{22:jotb22,
optkey = {},
author = {Iacopo Colonnelli and Dario Tranchitella},
title = {{OpenDeepHealth}: Crafting a Deep Learning Platform as a Service with {K}ubernetes},
howpublished = {J on The Beach 2022},
month = {April},
year = {2022},
keywords = {streamflow, jupyter-workflow, across, deephealth, hpc4ai},
address = {Malaga, Spain},
optannote = {},
url = {https://datacloud.di.unito.it/index.php/s/n6J7STNnwdyqtET}
}

I. Colonnelli, Distributed workflows with Jupyter, J on The Beach 2022 Malaga, Spain: , Workshop, April, 2022.
[BibTeX] [Download PDF]

@misc{22:jotb22-workshop,
optkey = {},
author = {Iacopo Colonnelli},
title = {Distributed workflows with {J}upyter},
howpublished = {J on The Beach 2022},
month = {April},
year = {2022},
keywords = {jupyter-workflow, deephealth, across},
address = {Malaga, Spain},
optannote = {},
note = {Workshop},
url = {https://datacloud.di.unito.it/index.php/s/om89q55S6ePf2Ji}
}

I. Colonnelli, StreamFlow: A framework for hybrid workflows, EUPEX WP5 bi-weekly meeting Virtual event: , April, 2022.
[BibTeX] [Download PDF]

@misc{22:eupex-streamflow,
optkey = {},
author = {Iacopo Colonnelli},
title = {{StreamFlow}: A framework for hybrid workflows},
howpublished = {EUPEX WP5 bi-weekly meeting},
month = {April},
year = {2022},
keywords = {streamflow, eupex},
address = {Virtual event},
optannote = {},
url = {https://datacloud.di.unito.it/index.php/s/NjKEySP7HfrCQHZ}
}

I. Colonnelli, StreamFlow: A framework for hybrid workflows, ACROSS WP4 meeting Virtual event: , February, 2022.
[BibTeX] [Download PDF]

@misc{22:across-streamflow,
optkey = {},
author = {Iacopo Colonnelli},
title = {{StreamFlow}: A framework for hybrid workflows},
howpublished = {ACROSS WP4 meeting},
month = {February},
year = {2022},
keywords = {streamflow, across},
address = {Virtual event},
optannote = {},
url = {https://datacloud.di.unito.it/index.php/s/FXFTKtQSRf6anMX}
}

I. Colonnelli, StreamFlow: A framework for hybrid workflows, ACROSS WP4 meeting Virtual event: , October, 2021.
[BibTeX] [Download PDF]

@misc{21:across-streamflow,
optkey = {},
author = {Iacopo Colonnelli},
title = {{StreamFlow}: A framework for hybrid workflows},
howpublished = {ACROSS WP4 meeting},
month = {October},
year = {2021},
keywords = {streamflow, across},
address = {Virtual event},
optannote = {},
url = {https://datacloud.di.unito.it/index.php/s/yrGYJL6CyNywF8a}
}

Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g., clouds, supercomputers, and both of them. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments (such as Kubernetes and SLURM), making it possible to execute onto multiple sites not sharing a common data space. Streamflow clearly distinguishes it from many other workflow management systems because it decouples the data dependencies from the deployment of (containerized) workflow steps. Streamflow also leverages CAPIO (Cross-Application Programmable I/O) to move data from one step to another efficiently. CAPIO captures the POSIX file system and streams it in parallel and in-memory to the workflow’s next step, possibly enabling in-transit data filtering.

@misc{21:eni:streamflow,
abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g., clouds, supercomputers, and both of them. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments (such as Kubernetes and SLURM), making it possible to execute onto multiple sites not sharing a common data space. Streamflow clearly distinguishes it from many other workflow management systems because it decouples the data dependencies from the deployment of (containerized) workflow steps. Streamflow also leverages CAPIO (Cross-Application Programmable I/O) to move data from one step to another efficiently. CAPIO captures the POSIX file system and streams it in parallel and in-memory to the workflow's next step, possibly enabling in-transit data filtering.},
address = {Virtual event},
author = {Marco Aldinucci},
howpublished = {Fifth EAGE Workshop on High Performance Computing for Upstream},
keywords = {keynote, streamflow, deephealth, across, admire},
month = {September},
note = {Keynote talk},
title = {The modernization of {HPC} applications for the cloud era},
year = {2021}
}

I. Colonnelli, HPC Containers, ACROSS WP4 meeting Virtual event: , July, 2021.
[BibTeX] [Download PDF]

@misc{21:across-containers,
optkey = {},
author = {Iacopo Colonnelli},
title = {{HPC} Containers},
howpublished = {ACROSS WP4 meeting},
month = {July},
year = {2021},
keywords = {across},
address = {Virtual event},
optannote = {},
url = {https://datacloud.di.unito.it/index.php/s/ddf3YBjpm8KBGAF}
}

M. Aldinucci, Reproducibility in the AI era, Penta Scientific Meeting Virtual event: , July, 2021.
[BibTeX] [Abstract] [Download PDF]

TBD

@misc{21:penta:covid,
abstract = {TBD},
address = {Virtual event},
author = {Marco Aldinucci},
howpublished = {Penta Scientific Meeting},
keywords = {invited, deephealth, across, admire},
month = {July},
title = {Reproducibility in the {AI} era},
url = {https://datacloud.di.unito.it/index.php/s/GLpf7kKSJRH733A},
year = {2021},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/GLpf7kKSJRH733A}
}

Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments and that makes it possible to execute multiple sites not sharing a common data space. StreamFlow supports both task and data parallelism and enables the reproducible and scalable execution of workflows, such as AI pipelines, in hybrid cloud-HPC environments. As a running example, we use the novel “universal COVID-19 pipeline” that explore the whole optimisation space of the training of different DNNs to classify COVID-19 lung lesions.

@misc{21:hlpp:streamflow,
abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments and that makes it possible to execute multiple sites not sharing a common data space. StreamFlow supports both task and data parallelism and enables the reproducible and scalable execution of workflows, such as AI pipelines, in hybrid cloud-HPC environments. As a running example, we use the novel ``universal COVID-19 pipeline'' that explore the whole optimisation space of the training of different DNNs to classify COVID-19 lung lesions.},
address = {Virtual event},
author = {Marco Aldinucci},
howpublished = {14th Intl. Symposium on High-Level Programming and Applications (HLPP)},
keywords = {keynote, streamflow, deephealth, across, admire},
month = jul,
title = {From skeletons to workflows in the cloud-edge era},
url = {https://datacloud.di.unito.it/index.php/s/RyRPjNBse5PKnab},
year = {2021},
note = {Keynote talk},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/RyRPjNBse5PKnab}
}

M. Aldinucci, The Italian research on HPC key technologies across EuroHPC Virtual Conference, Italy: ACM, 2021.
[BibTeX] [Abstract] [Download PDF]

High-Performance Computing (HPC) is one of the strategic priorities for research and innovation worldwide due to its relevance for industrial and scientific applications. We envision HPC as composed of three pillars: infrastructures, applications, and key technologies and tools. While infrastructures are by construction centralized in large-scale HPC centers, and applications are generally within the purview of domain-specific organizations, key technologies fall in an intermediate case where coordination is needed, but design and development are often decentralized. A large group of Italian researchers has started a dedicated laboratory within the National Interuniversity Consortium for Informatics (CINI) to address this challenge. The laboratory, albeit young, has managed to succeed in its first attempts to propose a coordinated approach to HPC research within the EuroHPC Joint Undertaking, participating in the calls 2019-20 to five successful proposals for an aggregate total cost of 95M Euro. In this paper, we outline the working group’s scope and goals and provide an overview of the five funded projects, which become fully operational in March 2021, and cover a selection of key technologies provided by the working group partners, highlighting their usage development within the projects.

@misc{21:CINI_acm_CF_talk,
abstract = {High-Performance Computing (HPC) is one of the strategic priorities for research and innovation worldwide due to its relevance for industrial and scientific applications. We envision HPC as composed of three pillars: infrastructures, applications, and key technologies and tools. While infrastructures are by construction centralized in large-scale HPC centers, and applications are generally within the purview of domain-specific organizations, key technologies fall in an intermediate case where coordination is needed, but design and development are often decentralized. A large group of Italian researchers has started a dedicated laboratory within the National Interuniversity Consortium for Informatics (CINI) to address this challenge. The laboratory, albeit young, has managed to succeed in its first attempts to propose a coordinated approach to HPC research within the EuroHPC Joint Undertaking, participating in the calls 2019-20 to five successful proposals for an aggregate total cost of 95M Euro. In this paper, we outline the working group's scope and goals and provide an overview of the five funded projects, which become fully operational in March 2021, and cover a selection of key technologies provided by the working group partners, highlighting their usage development within the projects.},
address = {Virtual Conference, Italy},
author = {Marco Aldinucci},
booktitle = {{ACM Computing Frontiers}},
keywords = {eurohpc, across, admire, textarossa, eupex, eupilot},
month = may,
publisher = {{ACM}},
title = {The {Italian} research on {HPC} key technologies across {EuroHPC}},
url = {https://datacloud.di.unito.it/index.php/s/3ZYmDbEm84rbB9k},
year = {2021},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/3ZYmDbEm84rbB9k}
}

Talks related to the HPC4AI project

M. Aldinucci, Experimenting with Systems for Decentralized Machine Learning, NVidia GTC 2023 , March, 2023.
[BibTeX] [Abstract] [Download PDF]

Decentralized machine learning (DML) enables collaborative machine learning without centralized input data. Federated learning (FL) and edge inference (EI) are examples of DML. Collaboration naturally happens at the edge of a distributed system with inherently distributed data. While tools for DML are starting to flourish, much needs to be done to get more flexible and portable tools to experiment with novel techniques, non-fully connected topologies, multiple data domains, and asynchronous collaboration schemes. We’ll present recent advances in DML, aiming to improve usability in data centers and, at the edge, to widen the class of models extending FL to non-DNN paradigms, to improve the accuracy of models controlling normalization and frequency of communications, and to boost data privacy though generative adversarial networks. Prerequisites: Intermediate understanding of machine learning methods and distributed & parallel computing.

@misc{23:gtc:fl,
optkey = {},
author = {Marco Aldinucci},
title = {Experimenting with Systems for Decentralized Machine Learning},
howpublished = {NVidia GTC 2023},
month = {March},
year = {2023},
optnote = {},
optannote = {},
keywords = {hpc4ai, eupex, across, textarossa, admire, eupilot, epi, space, eumaster4hpc},
abstract = {Decentralized machine learning (DML) enables collaborative machine learning without centralized input data. Federated learning (FL) and edge inference (EI) are examples of DML. Collaboration naturally happens at the edge of a distributed system with inherently distributed data. While tools for DML are starting to flourish, much needs to be done to get more flexible and portable tools to experiment with novel techniques, non-fully connected topologies, multiple data domains, and asynchronous collaboration schemes. We'll present recent advances in DML, aiming to improve usability in data centers and, at the edge, to widen the class of models extending FL to non-DNN paradigms, to improve the accuracy of models controlling normalization and frequency of communications, and to boost data privacy though generative adversarial networks. Prerequisites: Intermediate understanding of machine learning methods and distributed & parallel computing.},
url = {https://datacloud.di.unito.it/index.php/s/oyLt7xwkbKxz65c}
}

M. Aldinucci, HPC4AI: The Research on AI beyond the public cloud, CENTAI kick-off meeting Torino, Italy: , March, 2023.
[BibTeX] [Download PDF]

@misc{23:CENTAI:hpc4ai,
optkey = {},
author = {Marco Aldinucci},
title = {{HPC4AI}: The Research on {AI} beyond the public cloud},
howpublished = {CENTAI kick-off meeting},
month = {March},
year = {2023},
address = {Torino, Italy},
optnote = {},
optannote = {},
keywords = {invited, hpc4ai, eupex, across, textarossa, admire, eupilot, epi, eumaster4hpc, space, brainteaser},
abstract = {},
url = {https://datacloud.di.unito.it/index.php/s/PZXjPm8sfKTmTGb}
}

M. Aldinucci, From HPC4AI to ICSC living lab: Where systems are the research, Dell Advanced Computing Workshop 2023: HPC and Beyond Bologna, Italy: , Feb, 2023.
[BibTeX] [Download PDF]

@misc{23:Dell:hpc4ai,
optkey = {},
author = {Marco Aldinucci},
title = {From {HPC4AI} to {ICSC} living lab: Where systems are the research},
howpublished = {Dell Advanced Computing Workshop 2023: HPC and Beyond},
month = {Feb},
year = {2023},
address = {Bologna, Italy},
optnote = {},
optannote = {},
keywords = {invited, hpc4ai, futurehpc, eupex, textarossa, admire, eupilot},
abstract = {},
url = {https://datacloud.di.unito.it/index.php/s/M5QRJyDxyxokcfL}
}

I. Colonnelli and D. Tranchitella, Dossier: multi-tenant distributed Jupyter Notebooks, DoK Talks 141 Virtual event: , Invited talk, July, 2022.
[BibTeX] [Abstract] [Download PDF]

When providing data analysis as a service, one must tackle several problems. Data privacy and protection by design are crucial when working on sensitive data. Performance and scalability are fundamental for compute-intensive workloads, e.g. training Deep Neural Networks. User-friendly interfaces and fast prototyping tools are essential to allow domain experts to experiment with new techniques. Portability and reproducibility are necessary to assess the actual value of results. Kubernetes is the best platform to provide reliable, elastic, and maintainable services. However, Kubernetes alone is not enough to achieve large-scale multi-tenant reproducible data analysis. OOTB support for multi-tenancy is too rough, with only two levels of segregation (i.e. the single namespace or the entire cluster). Offloading computation to off-cluster resources is non-trivial and requires the user’s manual configuration. Also, Jupyter Notebooks per se cannot provide much scalability (they execute locally and sequentially) and reproducibility (users can run cells in any order and any number of times). The Dossier platform allows system administrators to manage multi-tenant distributed Jupyter Notebooks at the cluster level in the Kubernetes way, i.e. through CRDs. Namespaces are aggregated in Tenants, and all security and accountability aspects are managed at that level. Each Notebook spawns into a user-dedicated namespace, subject to all Tenant-level constraints. Users can rely on provisioned resources, either in-cluster worker nodes or external resources like HPC facilities. Plus, they can plug their computing nodes in a BYOD fashion. Notebooks are interpreted as distributed workflows, where each cell is a task that one can offload to a different location in charge of its execution.

@misc{22:data-on-kubernetes,
optkey = {},
author = {Iacopo Colonnelli and Dario Tranchitella},
abstract = {When providing data analysis as a service, one must tackle several problems. Data privacy and protection by design are crucial when working on sensitive data. Performance and scalability are fundamental for compute-intensive workloads, e.g. training Deep Neural Networks. User-friendly interfaces and fast prototyping tools are essential to allow domain experts to experiment with new techniques. Portability and reproducibility are necessary to assess the actual value of results. Kubernetes is the best platform to provide reliable, elastic, and maintainable services. However, Kubernetes alone is not enough to achieve large-scale multi-tenant reproducible data analysis. OOTB support for multi-tenancy is too rough, with only two levels of segregation (i.e. the single namespace or the entire cluster). Offloading computation to off-cluster resources is non-trivial and requires the user's manual configuration. Also, Jupyter Notebooks per se cannot provide much scalability (they execute locally and sequentially) and reproducibility (users can run cells in any order and any number of times). The Dossier platform allows system administrators to manage multi-tenant distributed Jupyter Notebooks at the cluster level in the Kubernetes way, i.e. through CRDs. Namespaces are aggregated in Tenants, and all security and accountability aspects are managed at that level. Each Notebook spawns into a user-dedicated namespace, subject to all Tenant-level constraints. Users can rely on provisioned resources, either in-cluster worker nodes or external resources like HPC facilities. Plus, they can plug their computing nodes in a BYOD fashion. Notebooks are interpreted as distributed workflows, where each cell is a task that one can offload to a different location in charge of its execution.},
title = {Dossier: multi-tenant distributed {J}upyter {N}otebooks},
howpublished = {DoK Talks 141},
month = {July},
year = {2022},
keywords = {jupyter-workflow, across, deephealth, hpc4ai},
note = {Invited talk},
address = {Virtual event},
optannote = {},
url = {https://datacloud.di.unito.it/index.php/s/RNqTGmTqWS66qHT}
}

M. Aldinucci, Da HPC4AI al living lab dello spoke FutureHPC del Centro Nazionale HPC, Condivisioni, Conferenza GARR 2022 Palermo, Italy: , Keynote talk, May, 2022.
[BibTeX] [Abstract] [Download PDF]

HPC4AI is an open-access laboratory of the University of Turin open to researchers, students and companies that manages a double pair of systems: a production cloud-HPC system and its twin dedicated to development. The cloud-HPC system is implemented thanks to an extended version of the GARR cloud (OpenStack) and the SLURM workload manager. HPC4AI is specifically designed to support system software development and cloud-HPC convergence tools. Among these streamflow (WMS), jupyter-as-a-service (SaaS), portable-secure-tenant (PasS). The experience gained in the design and management of HPC4AI forms the heart of the design of the livinglab of the Turin “FutureHPC” spoke of the National Center “HPC, BigData and Quantum Computing” funded by the PNRR which should be operational from September 2022.

@misc{22:garr,
optkey = {},
author = {Marco Aldinucci},
title = {Da {HPC4AI} al living lab dello spoke {FutureHPC} del Centro Nazionale {HPC}},
howpublished = {Condivisioni, Conferenza GARR 2022},
month = {May},
year = {2022},
keywords = {keynote, hpc4ai, across, eupex, across, admire, textarossa, eumaster4hpc, icsc},
address = {Palermo, Italy},
note = {Keynote talk},
optannote = {},
abstract = {HPC4AI is an open-access laboratory of the University of Turin open to researchers, students and companies that manages a double pair of systems: a production cloud-HPC system and its twin dedicated to development. The cloud-HPC system is implemented thanks to an extended version of the GARR cloud (OpenStack) and the SLURM workload manager. HPC4AI is specifically designed to support system software development and cloud-HPC convergence tools. Among these streamflow (WMS), jupyter-as-a-service (SaaS), portable-secure-tenant (PasS). The experience gained in the design and management of HPC4AI forms the heart of the design of the livinglab of the Turin "FutureHPC" spoke of the National Center "HPC, BigData and Quantum Computing" funded by the PNRR which should be operational from September 2022.},
url = {https://datacloud.di.unito.it/index.php/s/P3KSroSSmrRxZMc}
}

I. Colonnelli and D. Tranchitella, OpenDeepHealth: Crafting a Deep Learning Platform as a Service with Kubernetes, J on The Beach 2022 Malaga, Spain: , April, 2022.
[BibTeX] [Download PDF]

@misc{22:jotb22,
optkey = {},
author = {Iacopo Colonnelli and Dario Tranchitella},
title = {{OpenDeepHealth}: Crafting a Deep Learning Platform as a Service with {K}ubernetes},
howpublished = {J on The Beach 2022},
month = {April},
year = {2022},
keywords = {streamflow, jupyter-workflow, across, deephealth, hpc4ai},
address = {Malaga, Spain},
optannote = {},
url = {https://datacloud.di.unito.it/index.php/s/n6J7STNnwdyqtET}
}

I. Colonnelli, The OpenDeepHealth toolkit, DeepHealth Winter School Torino, Italy: , January, 2022.
[BibTeX] [Download PDF]

@misc{22:DHWinterSchool,
address = {Torino, Italy},
author = {Iacopo Colonnelli},
howpublished = {DeepHealth Winter School},
keywords = {deephealth, hpc4ai},
month = {January},
title = {The {OpenDeepHealth} toolkit},
url = {https://datacloud.di.unito.it/index.php/s/cJ8pRNsWRrfwPqr},
year = {2022},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/cJ8pRNsWRrfwPqr}
}

M. Aldinucci and S. Rabellino, , Vertiv keep it running tour Milano, Italy: , Invited talk, November, 2021.
[BibTeX] [Download PDF]

@misc{21:vertiv,
optkey = {},
author = {Marco Aldinucci and Sergiuo Rabellino},
opttitle = {HPC4AI Green Datacenter Design},
howpublished = {Vertiv keep it running tour},
month = {November},
year = {2021},
keywords = {invited, hpc4ai},
address = {Milano, Italy},
note = {Invited talk},
optannote = {},
url = {https://datacloud.di.unito.it/index.php/s/y6afrJr9w2DTmRN}
}

M. Aldinucci and M. Beccuti, HPC4AI: Un sistema per la ricerca e l’innovazione dei servizi cloud per l’Intelligenza Artificiale, Reserach meeting of the PoloICT Torino, Italy: , apr, 2021.
[BibTeX] [Download PDF]

@misc{21:poloict_hpc4ai,
address = {Torino, Italy},
author = {Marco Aldinucci and Marco Beccuti},
date-added = {2021-08-01 16:15:57 +0200},
date-modified = {2021-08-01 16:19:11 +0200},
howpublished = {Reserach meeting of the PoloICT},
keywords = {hpc4ai},
month = apr,
title = {{HPC4AI}: Un sistema per la ricerca e l'innovazione dei servizi cloud per l'Intelligenza Artificiale},
url = {https://datacloud.di.unito.it/index.php/s/BXdXLzsisQwDLrK},
year = {2021}
}

M. Aldinucci and I. Colonnelli, The Universal Cloud-HPC Pipeline for the AI-Assisted Explainable Diagnosis of COVID-19 Pneumonia, NVidia GTC’21 Virtual event: , Invited talk, April, 2021.
[BibTeX] [Abstract] [Download PDF]

We’ll present a methodology to run DNN pipelines on hybrid cloud+HPC infrastructure. We’ll also define a “universal pipeline” for medical images. The pipeline can reproduce all state-of-the-art DNNs to diagnose COVID-19 pneumonia, which appeared in the literature during the first Italian lockdown and following months. We can run all of them (across cloud+HPC platforms) and compare their performance in terms of sensitivity and specificity to set a baseline to evaluate future progress in the automated diagnosis of COVID-19. Also, the pipeline makes existing DNNs explainable by way of adversarial training. The pipeline is easily portable and can run across different infrastructures, adapting the performance-urgency trade-off. The methodology builds onto two novel software programs: the streamflow workflow system and the AI-sandbox concept (parallel container with user-space encrypted file system). We reach over 92\% accuracy in diagnosing COVID pneumonia.

@misc{21:gtc:clairecovid,
abstract = {We'll present a methodology to run DNN pipelines on hybrid cloud+HPC infrastructure. We'll also define a "universal pipeline" for medical images. The pipeline can reproduce all state-of-the-art DNNs to diagnose COVID-19 pneumonia, which appeared in the literature during the first Italian lockdown and following months. We can run all of them (across cloud+HPC platforms) and compare their performance in terms of sensitivity and specificity to set a baseline to evaluate future progress in the automated diagnosis of COVID-19. Also, the pipeline makes existing DNNs explainable by way of adversarial training. The pipeline is easily portable and can run across different infrastructures, adapting the performance-urgency trade-off. The methodology builds onto two novel software programs: the streamflow workflow system and the AI-sandbox concept (parallel container with user-space encrypted file system). We reach over 92\% accuracy in diagnosing COVID pneumonia.},
address = {Virtual event},
author = {Marco Aldinucci and Iacopo Colonnelli},
howpublished = {NVidia GTC'21},
keywords = {invited, streamflow, deephealth, hpc4ai},
month = {April},
note = {Invited talk},
title = {The Universal Cloud-{HPC} Pipeline for the {AI}-Assisted Explainable Diagnosis of {COVID-19} Pneumonia},
url = {https://datacloud.di.unito.it/index.php/s/AkQLbPpEEtDzbbm},
year = {2021},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/AkQLbPpEEtDzbbm}
}

M. Aldinucci, HPC application cloudification: the streamflow toolkit, PARMA-DITAM (co-localed with HiPEAC) Virtual event: , Keynote talk, January, 2021.
[BibTeX] [Download PDF]

@misc{21:parmaditam:hpc4ai,
address = {Virtual event},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {PARMA-DITAM (co-localed with HiPEAC)},
keywords = {keynote, hpc4ai, deephealth},
month = {January},
title = {{HPC} application cloudification: the streamflow toolkit},
url = {https://datacloud.di.unito.it/index.php/s/HWZijXPqmwfoYCp},
year = {2021},
note = {Keynote talk},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2021_PARMA-DITAM_keynote_HPC-Cloudification.pdf}
}

M. Aldinucci, On HPC, AI and their Fatal Attraction, CNR IEIIT, Thursday seminars (11 Feb 2021) Virtual event: , Invited talk, 2021.
[BibTeX] [Download PDF]

@misc{21:CNR:hpcai,
address = {Virtual event},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {CNR IEIIT, Thursday seminars (11 Feb 2021)},
keywords = {invited, hpc4ai, deephealth},
month = feb,
note = {Invited talk},
title = {On {HPC}, {AI} and their Fatal Attraction},
url = {https://datacloud.di.unito.it/index.php/s/pSDxNPncic8gEy8},
year = {2021},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2021_CNR_HPC+AI.pdf}
}

M. Aldinucci and M. Beccuti, DeepHealth: Deep Learning ad alte prestazioni per applicazioni in ambito medico, Reserach meeting of the PoloICT Torino, Italy: , 2021.
[BibTeX] [Download PDF]

@misc{21:poloict:deephealth,
address = {Torino, Italy},
author = {Marco Aldinucci and Marco Beccuti},
date-added = {2021-08-01 16:19:46 +0200},
date-modified = {2021-08-01 16:20:45 +0200},
howpublished = {Reserach meeting of the PoloICT},
keywords = {deephealth, hpc4ai},
month = apr,
title = {{DeepHealth}: Deep Learning ad alte prestazioni per applicazioni in ambito medico},
url = {https://datacloud.di.unito.it/index.php/s/2F5Net5HdfJTysa},
year = {2021},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/BXdXLzsisQwDLrK}
}

M. Aldinucci, HPC4AI: A cloud-HPC ecosystem designed for research and innovation, Advanced Computing Workshop 2021: HPC and Beyond , Invited talk, 2021.
[BibTeX] [Abstract] [Download PDF]

The University of Turin and Polytechnic University of Turin have joined forces to create a federated competence centre on High-Performance Computing (HPC), Artificial Intelligence (AI) and Big Data Analytics (BDA). HPC4AI is designed as a centre capable of collaborating with entrepreneurs to boost their ability to innovate on data-driven technologies and applications. HPC4AI started in 2017 with the construction of four new federated computing laboratories completed at the end of 2020. HPC4AI is organized in two poles: one at the University of Turin (UNITO), which coordinated the design and implementation phase of HPC4AI, and another at the Polytechnic of Turin (POLITO).

@misc{20:dell:hpc4ai,
abstract = {The University of Turin and Polytechnic University of Turin have joined forces to create a federated competence centre on High-Performance Computing (HPC), Artificial Intelligence (AI) and Big Data Analytics (BDA). HPC4AI is designed as a centre capable of collaborating with entrepreneurs to boost their ability to innovate on data-driven technologies and applications.
HPC4AI started in 2017 with the construction of four new federated computing laboratories completed at the end of 2020. HPC4AI is organized in two poles: one at the University of Turin (UNITO), which coordinated the design and implementation phase of HPC4AI, and another at the Polytechnic of Turin (POLITO).
},
author = {Marco Aldinucci},
date-added = {2021-01-21 13:07:40 +0000},
date-modified = {2021-01-21 13:12:29 +0000},
howpublished = {Advanced Computing Workshop 2021: HPC and Beyond},
keywords = {invited, hpc4ai},
month = jan,
note = {Invited talk},
title = {{HPC4AI}: A cloud-{HPC} ecosystem designed for research and innovation},
url = {https://datacloud.di.unito.it/index.php/s/NmM9kB42pJZGMx6},
year = {2021},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/NmM9kB42pJZGMx6}
}

I. Colonnelli and S. Rabellino, JupyterFlow: Jupyter Notebooks su larga scala, Workshop GARR 2020 Virtual event: , November, 2020.
[BibTeX] [Abstract] [Download PDF]

I Jupyter Notebook sono largamente utilizzati sia in ambito industriale che accademico come strumento di didattica, prototipazione e analisi esplorative. Purtroppo il sistema runtime standard di Jupyter non è abbastanza potente per sostenere un carichi di lavoro reali e spesso l’unica soluzione è quella di riscrivere il codice da zero in una tecnologia con supporto HPC. Intrgrando lo stack Jupyter con StreamFlow (https://streamflow.di.unito.it/) è possibile creare i Notebook tramite un’interfaccia web su cloud ed eseguirli in maniera trasparente in remoto su una VM con GPU o su nodi HPC.

@misc{20:GarrWorkshop,
abstract = {I Jupyter Notebook sono largamente utilizzati sia in ambito industriale che accademico come strumento di didattica, prototipazione e analisi esplorative. Purtroppo il sistema runtime standard di Jupyter non \`{e} abbastanza potente per sostenere un carichi di lavoro reali e spesso l'unica soluzione \`{e} quella di riscrivere il codice da zero in una tecnologia con supporto HPC. Intrgrando lo stack Jupyter con StreamFlow (https://streamflow.di.unito.it/) \`{e} possibile creare i Notebook tramite un'interfaccia web su cloud ed eseguirli in maniera trasparente in remoto su una VM con GPU o su nodi HPC.},
address = {Virtual event},
author = {Iacopo Colonnelli and Sergio Rabellino},
howpublished = {Workshop GARR 2020},
keywords = {jupyter-workflow, hpc4ai, deephealth},
month = {November},
title = {{JupyterFlow}: {J}upyter {N}otebooks su larga scala},
url = {https://datacloud.di.unito.it/index.php/s/ASPEmyXAj5QscgC},
year = {2020},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/ASPEmyXAj5QscgC},
bdsk-url-2 = {https://www.eventi.garr.it/it/ws20/programma/speaker/680-iacopo-colonnelli}
}

M. Aldinucci, Machine Learning: the treacherous journey from data to knowledge (with examples from HPC4AI@UNITO platform), Machine Learning Meets Chemistry @ the Department of Chemistry, University of Torino Torino, Italy: , Invited talk, feb, 2020.
[BibTeX] [Download PDF]

@misc{20:chem:HPCAI,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {Machine Learning Meets Chemistry @ the Department of Chemistry, University of Torino},
keywords = {invited, hpc4ai, deephealth},
month = feb,
note = {Invited talk},
title = {Machine Learning: the treacherous journey from data to knowledge (with examples from {HPC4AI@UNITO} platform)},
url = {https://datacloud.di.unito.it/index.php/s/ffyZYYqNQpkza4F},
year = {2020},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2020_ML-chemistry.pdf}
}

M. Aldinucci, HPC4AI: From Enabling Platforms to Technology Sovereignty to Innovation, Elixir-Italia meeting Torino, Italy: , Invited talk, February, 2020.
[BibTeX] [Download PDF]

@misc{20:elixir:hpc4ai,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {Elixir-Italia meeting},
keywords = {invited, hpc4ai},
month = {February},
note = {Invited talk},
title = {{HPC4AI}: From Enabling Platforms to Technology Sovereignty to Innovation},
url = {https://datacloud.di.unito.it/index.php/s/g6LZiErXH4PPRCj},
year = {2020},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2020_02_Elixir_HPC4AI.pdf}
}

M. Aldinucci, HPC4AI, an on-demand federated platform endeavour, Ospedale San Raffaele Milano, Italy: , Invited talk, 2019.
[BibTeX] [Download PDF]

@misc{19:SR:hpc4ai,
address = {Milano, Italy},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {Ospedale San Raffaele},
keywords = {invited, hpc4ai},
month = may,
note = {Invited talk},
title = {{HPC4AI}, an on-demand federated platform endeavour},
url = {https://datacloud.di.unito.it/index.php/s/P7fxgExkJDAFQbm},
year = {2019},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2019_SanRaffaele.pdf}
}

Talks related to the Deephealth project

I. Colonnelli and D. Tranchitella, Dossier: multi-tenant distributed Jupyter Notebooks, DoK Talks 141 Virtual event: , Invited talk, July, 2022.
[BibTeX] [Abstract] [Download PDF]

When providing data analysis as a service, one must tackle several problems. Data privacy and protection by design are crucial when working on sensitive data. Performance and scalability are fundamental for compute-intensive workloads, e.g. training Deep Neural Networks. User-friendly interfaces and fast prototyping tools are essential to allow domain experts to experiment with new techniques. Portability and reproducibility are necessary to assess the actual value of results. Kubernetes is the best platform to provide reliable, elastic, and maintainable services. However, Kubernetes alone is not enough to achieve large-scale multi-tenant reproducible data analysis. OOTB support for multi-tenancy is too rough, with only two levels of segregation (i.e. the single namespace or the entire cluster). Offloading computation to off-cluster resources is non-trivial and requires the user’s manual configuration. Also, Jupyter Notebooks per se cannot provide much scalability (they execute locally and sequentially) and reproducibility (users can run cells in any order and any number of times). The Dossier platform allows system administrators to manage multi-tenant distributed Jupyter Notebooks at the cluster level in the Kubernetes way, i.e. through CRDs. Namespaces are aggregated in Tenants, and all security and accountability aspects are managed at that level. Each Notebook spawns into a user-dedicated namespace, subject to all Tenant-level constraints. Users can rely on provisioned resources, either in-cluster worker nodes or external resources like HPC facilities. Plus, they can plug their computing nodes in a BYOD fashion. Notebooks are interpreted as distributed workflows, where each cell is a task that one can offload to a different location in charge of its execution.

@misc{22:data-on-kubernetes,
optkey = {},
author = {Iacopo Colonnelli and Dario Tranchitella},
abstract = {When providing data analysis as a service, one must tackle several problems. Data privacy and protection by design are crucial when working on sensitive data. Performance and scalability are fundamental for compute-intensive workloads, e.g. training Deep Neural Networks. User-friendly interfaces and fast prototyping tools are essential to allow domain experts to experiment with new techniques. Portability and reproducibility are necessary to assess the actual value of results. Kubernetes is the best platform to provide reliable, elastic, and maintainable services. However, Kubernetes alone is not enough to achieve large-scale multi-tenant reproducible data analysis. OOTB support for multi-tenancy is too rough, with only two levels of segregation (i.e. the single namespace or the entire cluster). Offloading computation to off-cluster resources is non-trivial and requires the user's manual configuration. Also, Jupyter Notebooks per se cannot provide much scalability (they execute locally and sequentially) and reproducibility (users can run cells in any order and any number of times). The Dossier platform allows system administrators to manage multi-tenant distributed Jupyter Notebooks at the cluster level in the Kubernetes way, i.e. through CRDs. Namespaces are aggregated in Tenants, and all security and accountability aspects are managed at that level. Each Notebook spawns into a user-dedicated namespace, subject to all Tenant-level constraints. Users can rely on provisioned resources, either in-cluster worker nodes or external resources like HPC facilities. Plus, they can plug their computing nodes in a BYOD fashion. Notebooks are interpreted as distributed workflows, where each cell is a task that one can offload to a different location in charge of its execution.},
title = {Dossier: multi-tenant distributed {J}upyter {N}otebooks},
howpublished = {DoK Talks 141},
month = {July},
year = {2022},
keywords = {jupyter-workflow, across, deephealth, hpc4ai},
note = {Invited talk},
address = {Virtual event},
optannote = {},
url = {https://datacloud.di.unito.it/index.php/s/RNqTGmTqWS66qHT}
}

@misc{22:healthycloud-workshop,
optkey = {},
author = {Iacopo Colonnelli},
title = {{StreamFlow}},
howpublished = {2nd HealthyCloud Workshop: Analysis of existing orchestration mechanisms for distributed computational analyses},
month = {July},
year = {2022},
keywords = {invited, streamflow, deephealth, across, eupex, textarossa},
note = {Invited talk},
address = {Virtual event},
optannote = {},
url = {https://datacloud.di.unito.it/index.php/s/Taz8qtzmkmn9ffT}
}

First, HPC infrastructures are embracing GPUs for their superior performance-per-watt ratio against general-purpose multicores. Second, the next-generation scientific workflows are integrating AI-based steps for their accuracy in approximating and analyzing complex phenomena. Third, AI and specifically Machine Learning (ML), is a perfect workload for GPUs in terms of performance and development time. Today, we cannot still close the circle seamlessly running AI-enabled scientific workloads into HPC infrastructures because their system software and development tools are not designed for modern workloads, such as ML frameworks designed for the cloud. HPC-cloud convergence is likely to bridge the gap. In the talk, we will present Streamflow and CAPIO, two development tools for HPC-cloud convergence.

@misc{22:ml4astro,
address = {Catania, Italy},
author = {Marco Aldinucci},
howpublished = {Machine Learning for Astrophysics {(ML4ASTRO)}},
keywords = {keynote, deephealth, eupex, across, eupilot},
month = {June},
title = {{HPC}-cloud convergence is the missing link between scientific computing and applied{-AI}},
note = {Keynote talk},
year = {2022},
url = {https://datacloud.di.unito.it/index.php/s/2SGswkcip7MoMoH},
abstract = {First, HPC infrastructures are embracing GPUs for their superior performance-per-watt ratio against general-purpose multicores. Second, the next-generation scientific workflows are integrating AI-based steps for their accuracy in approximating and analyzing complex phenomena. Third, AI and specifically Machine Learning (ML), is a perfect workload for GPUs in terms of performance and development time. Today, we cannot still close the circle seamlessly running AI-enabled scientific workloads into HPC infrastructures because their system software and development tools are not designed for modern workloads, such as ML frameworks designed for the cloud. HPC-cloud convergence is likely to bridge the gap. In the talk, we will present Streamflow and CAPIO, two development tools for HPC-cloud convergence.}
}

I. Colonnelli and D. Tranchitella, OpenDeepHealth: Crafting a Deep Learning Platform as a Service with Kubernetes, J on The Beach 2022 Malaga, Spain: , April, 2022.
[BibTeX] [Download PDF]

@misc{22:jotb22,
optkey = {},
author = {Iacopo Colonnelli and Dario Tranchitella},
title = {{OpenDeepHealth}: Crafting a Deep Learning Platform as a Service with {K}ubernetes},
howpublished = {J on The Beach 2022},
month = {April},
year = {2022},
keywords = {streamflow, jupyter-workflow, across, deephealth, hpc4ai},
address = {Malaga, Spain},
optannote = {},
url = {https://datacloud.di.unito.it/index.php/s/n6J7STNnwdyqtET}
}

I. Colonnelli, Distributed workflows with Jupyter, J on The Beach 2022 Malaga, Spain: , Workshop, April, 2022.
[BibTeX] [Download PDF]

@misc{22:jotb22-workshop,
optkey = {},
author = {Iacopo Colonnelli},
title = {Distributed workflows with {J}upyter},
howpublished = {J on The Beach 2022},
month = {April},
year = {2022},
keywords = {jupyter-workflow, deephealth, across},
address = {Malaga, Spain},
optannote = {},
note = {Workshop},
url = {https://datacloud.di.unito.it/index.php/s/om89q55S6ePf2Ji}
}

I. Colonnelli, The OpenDeepHealth toolkit, DeepHealth Winter School Torino, Italy: , January, 2022.
[BibTeX] [Download PDF]

@misc{22:DHWinterSchool,
address = {Torino, Italy},
author = {Iacopo Colonnelli},
howpublished = {DeepHealth Winter School},
keywords = {deephealth, hpc4ai},
month = {January},
title = {The {OpenDeepHealth} toolkit},
url = {https://datacloud.di.unito.it/index.php/s/cJ8pRNsWRrfwPqr},
year = {2022},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/cJ8pRNsWRrfwPqr}
}

Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g., clouds, supercomputers, and both of them. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments (such as Kubernetes and SLURM), making it possible to execute onto multiple sites not sharing a common data space. Streamflow clearly distinguishes it from many other workflow management systems because it decouples the data dependencies from the deployment of (containerized) workflow steps. Streamflow also leverages CAPIO (Cross-Application Programmable I/O) to move data from one step to another efficiently. CAPIO captures the POSIX file system and streams it in parallel and in-memory to the workflow’s next step, possibly enabling in-transit data filtering.

@misc{21:eni:streamflow,
abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g., clouds, supercomputers, and both of them. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments (such as Kubernetes and SLURM), making it possible to execute onto multiple sites not sharing a common data space. Streamflow clearly distinguishes it from many other workflow management systems because it decouples the data dependencies from the deployment of (containerized) workflow steps. Streamflow also leverages CAPIO (Cross-Application Programmable I/O) to move data from one step to another efficiently. CAPIO captures the POSIX file system and streams it in parallel and in-memory to the workflow's next step, possibly enabling in-transit data filtering.},
address = {Virtual event},
author = {Marco Aldinucci},
howpublished = {Fifth EAGE Workshop on High Performance Computing for Upstream},
keywords = {keynote, streamflow, deephealth, across, admire},
month = {September},
note = {Keynote talk},
title = {The modernization of {HPC} applications for the cloud era},
year = {2021}
}

M. Aldinucci, Reproducibility in the AI era, Penta Scientific Meeting Virtual event: , July, 2021.
[BibTeX] [Abstract] [Download PDF]

TBD

@misc{21:penta:covid,
abstract = {TBD},
address = {Virtual event},
author = {Marco Aldinucci},
howpublished = {Penta Scientific Meeting},
keywords = {invited, deephealth, across, admire},
month = {July},
title = {Reproducibility in the {AI} era},
url = {https://datacloud.di.unito.it/index.php/s/GLpf7kKSJRH733A},
year = {2021},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/GLpf7kKSJRH733A}
}

We’ll present a methodology to run DNN pipelines on hybrid cloud+HPC infrastructure. We’ll also define a “universal pipeline” for medical images. The pipeline can reproduce all state-of-the-art DNNs to diagnose COVID-19 pneumonia, which appeared in the literature during the first Italian lockdown and following months. We can run all of them (across cloud+HPC platforms) and compare their performance in terms of sensitivity and specificity to set a baseline to evaluate future progress in the automated diagnosis of COVID-19. Also, the pipeline makes existing DNNs explainable by way of adversarial training. The pipeline is easily portable and can run across different infrastructures, adapting the performance-urgency trade-off. The methodology builds onto two novel software programs: the streamflow workflow system and the AI-sandbox concept (parallel container with user-space encrypted file system). We reach over 92\% accuracy in diagnosing COVID pneumonia.

@misc{21:gtc:clairecovid,
abstract = {We'll present a methodology to run DNN pipelines on hybrid cloud+HPC infrastructure. We'll also define a "universal pipeline" for medical images. The pipeline can reproduce all state-of-the-art DNNs to diagnose COVID-19 pneumonia, which appeared in the literature during the first Italian lockdown and following months. We can run all of them (across cloud+HPC platforms) and compare their performance in terms of sensitivity and specificity to set a baseline to evaluate future progress in the automated diagnosis of COVID-19. Also, the pipeline makes existing DNNs explainable by way of adversarial training. The pipeline is easily portable and can run across different infrastructures, adapting the performance-urgency trade-off. The methodology builds onto two novel software programs: the streamflow workflow system and the AI-sandbox concept (parallel container with user-space encrypted file system). We reach over 92\% accuracy in diagnosing COVID pneumonia.},
address = {Virtual event},
author = {Marco Aldinucci and Iacopo Colonnelli},
howpublished = {NVidia GTC'21},
keywords = {invited, streamflow, deephealth, hpc4ai},
month = {April},
note = {Invited talk},
title = {The Universal Cloud-{HPC} Pipeline for the {AI}-Assisted Explainable Diagnosis of {COVID-19} Pneumonia},
url = {https://datacloud.di.unito.it/index.php/s/AkQLbPpEEtDzbbm},
year = {2021},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/AkQLbPpEEtDzbbm}
}

I. Colonnelli, StreamFlow: cross breeding cloud with HPC, 2021 CWL Mini Conference Virtual event: , Invited talk, February, 2021.
[BibTeX] [Abstract] [Download PDF]

Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space.

@misc{21:CWLMiniConference,
abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space.},
address = {Virtual event},
author = {Iacopo Colonnelli},
howpublished = {2021 CWL Mini Conference},
keywords = {invited, streamflow, deephealth},
month = {February},
note = {Invited talk},
title = {{StreamFlow}: cross breeding cloud with {HPC}},
url = {https://datacloud.di.unito.it/index.php/s/Le9gg4PfjRxBwXD},
year = {2021}
}

M. Aldinucci, HPC application cloudification: the streamflow toolkit, PARMA-DITAM (co-localed with HiPEAC) Virtual event: , Keynote talk, January, 2021.
[BibTeX] [Download PDF]

@misc{21:parmaditam:hpc4ai,
address = {Virtual event},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {PARMA-DITAM (co-localed with HiPEAC)},
keywords = {keynote, hpc4ai, deephealth},
month = {January},
title = {{HPC} application cloudification: the streamflow toolkit},
url = {https://datacloud.di.unito.it/index.php/s/HWZijXPqmwfoYCp},
year = {2021},
note = {Keynote talk},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2021_PARMA-DITAM_keynote_HPC-Cloudification.pdf}
}

M. Aldinucci and M. Beccuti, DeepHealth: Deep Learning ad alte prestazioni per applicazioni in ambito medico, Reserach meeting of the PoloICT Torino, Italy: , 2021.
[BibTeX] [Download PDF]

@misc{21:poloict:deephealth,
address = {Torino, Italy},
author = {Marco Aldinucci and Marco Beccuti},
date-added = {2021-08-01 16:19:46 +0200},
date-modified = {2021-08-01 16:20:45 +0200},
howpublished = {Reserach meeting of the PoloICT},
keywords = {deephealth, hpc4ai},
month = apr,
title = {{DeepHealth}: Deep Learning ad alte prestazioni per applicazioni in ambito medico},
url = {https://datacloud.di.unito.it/index.php/s/2F5Net5HdfJTysa},
year = {2021},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/BXdXLzsisQwDLrK}
}

Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments and that makes it possible to execute multiple sites not sharing a common data space. StreamFlow supports both task and data parallelism and enables the reproducible and scalable execution of workflows, such as AI pipelines, in hybrid cloud-HPC environments. As a running example, we use the novel “universal COVID-19 pipeline” that explore the whole optimisation space of the training of different DNNs to classify COVID-19 lung lesions.

@misc{21:hlpp:streamflow,
abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments and that makes it possible to execute multiple sites not sharing a common data space. StreamFlow supports both task and data parallelism and enables the reproducible and scalable execution of workflows, such as AI pipelines, in hybrid cloud-HPC environments. As a running example, we use the novel ``universal COVID-19 pipeline'' that explore the whole optimisation space of the training of different DNNs to classify COVID-19 lung lesions.},
address = {Virtual event},
author = {Marco Aldinucci},
howpublished = {14th Intl. Symposium on High-Level Programming and Applications (HLPP)},
keywords = {keynote, streamflow, deephealth, across, admire},
month = jul,
title = {From skeletons to workflows in the cloud-edge era},
url = {https://datacloud.di.unito.it/index.php/s/RyRPjNBse5PKnab},
year = {2021},
note = {Keynote talk},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/RyRPjNBse5PKnab}
}

M. Aldinucci, DeepHealth perspective, Future challenges in IoT, AI, and convergence of HPC & Cloud & Big Data – BDVA Data Week Virtual event: , 2021.
[BibTeX] [Abstract]

TBD

@misc{21:dataweek:deephealth,
abstract = {TBD},
address = {Virtual event},
author = {Marco Aldinucci},
howpublished = {Future challenges in IoT, AI, and convergence of HPC & Cloud & Big Data -- BDVA Data Week},
keywords = {invited, deephealth},
month = may,
title = {DeepHealth perspective},
year = {2021}
}

M. Aldinucci, Lung Nodules Segmentation in CT scans by DeepHealth toolkit, 25th Intl. Conference on Pattern Recognition Milano. Italy: BDVA, Demo, 2021.
[BibTeX] [Download PDF]

@misc{21:icpr:demodeephealth,
address = {Milano. Italy},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {25th Intl. Conference on Pattern Recognition},
keywords = {demo, deephealth},
month = jan,
note = {Demo},
publisher = {{BDVA}},
title = {Lung Nodules Segmentation in {CT} scans by DeepHealth toolkit},
url = {https://datacloud.di.unito.it/index.php/s/KYJMcT3pfpat2Hx},
year = {2021},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2021-ICPR20-S1.1-LungNodulesSegmentation-Presentation.pdf}
}

M. Aldinucci, On HPC, AI and their Fatal Attraction, CNR IEIIT, Thursday seminars (11 Feb 2021) Virtual event: , Invited talk, 2021.
[BibTeX] [Download PDF]

@misc{21:CNR:hpcai,
address = {Virtual event},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {CNR IEIIT, Thursday seminars (11 Feb 2021)},
keywords = {invited, hpc4ai, deephealth},
month = feb,
note = {Invited talk},
title = {On {HPC}, {AI} and their Fatal Attraction},
url = {https://datacloud.di.unito.it/index.php/s/pSDxNPncic8gEy8},
year = {2021},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2021_CNR_HPC+AI.pdf}
}

I. Colonnelli and S. Rabellino, JupyterFlow: Jupyter Notebooks su larga scala, Workshop GARR 2020 Virtual event: , November, 2020.
[BibTeX] [Abstract] [Download PDF]

I Jupyter Notebook sono largamente utilizzati sia in ambito industriale che accademico come strumento di didattica, prototipazione e analisi esplorative. Purtroppo il sistema runtime standard di Jupyter non è abbastanza potente per sostenere un carichi di lavoro reali e spesso l’unica soluzione è quella di riscrivere il codice da zero in una tecnologia con supporto HPC. Intrgrando lo stack Jupyter con StreamFlow (https://streamflow.di.unito.it/) è possibile creare i Notebook tramite un’interfaccia web su cloud ed eseguirli in maniera trasparente in remoto su una VM con GPU o su nodi HPC.

@misc{20:GarrWorkshop,
abstract = {I Jupyter Notebook sono largamente utilizzati sia in ambito industriale che accademico come strumento di didattica, prototipazione e analisi esplorative. Purtroppo il sistema runtime standard di Jupyter non \`{e} abbastanza potente per sostenere un carichi di lavoro reali e spesso l'unica soluzione \`{e} quella di riscrivere il codice da zero in una tecnologia con supporto HPC. Intrgrando lo stack Jupyter con StreamFlow (https://streamflow.di.unito.it/) \`{e} possibile creare i Notebook tramite un'interfaccia web su cloud ed eseguirli in maniera trasparente in remoto su una VM con GPU o su nodi HPC.},
address = {Virtual event},
author = {Iacopo Colonnelli and Sergio Rabellino},
howpublished = {Workshop GARR 2020},
keywords = {jupyter-workflow, hpc4ai, deephealth},
month = {November},
title = {{JupyterFlow}: {J}upyter {N}otebooks su larga scala},
url = {https://datacloud.di.unito.it/index.php/s/ASPEmyXAj5QscgC},
year = {2020},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/ASPEmyXAj5QscgC},
bdsk-url-2 = {https://www.eventi.garr.it/it/ws20/programma/speaker/680-iacopo-colonnelli}
}

M. Aldinucci, The DeepHealth project, HPC, Big Data, IoT and AI future industry-driven collaborative strategic topics virtual workshop –- HPC / HPDA spectrum BDVA, Invited talk, jul, 2020.
[BibTeX] [Download PDF]

@misc{20:bdva:deephealth,
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-02-14 18:48:35 +0100},
howpublished = {HPC, Big Data, IoT and AI future industry-driven collaborative strategic topics virtual workshop --- HPC / HPDA spectrum},
keywords = {invited, deephealth},
month = jul,
note = {Invited talk},
publisher = {BDVA},
title = {The DeepHealth project},
url = {https://datacloud.di.unito.it/index.php/s/ortwLJHS2q96irb},
year = {2020},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2020_DEEPHEALTH_ICT-11-projects-workshop-v3.0.pdf}
}

M. Aldinucci, Building avenues for AI-assisted diagnosis over the bridge from HPC to AI, CLAIRE COVID webinar Torino, Italy: , Invited talk, jul, 2020.
[BibTeX] [Download PDF]

@misc{20:claire:taskforce,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {CLAIRE COVID webinar},
keywords = {invited, deephealth, claire},
month = jul,
note = {Invited talk},
title = {Building avenues for AI-assisted diagnosis over the bridge from {HPC} to {AI}},
url = {https://datacloud.di.unito.it/index.php/s/RqpNCHyyL6wc5ds},
year = {2020},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2020_CLAIRE-Medical-Images-Taskforce-webinar.pdf}
}

@misc{20:chem:HPCAI,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {Machine Learning Meets Chemistry @ the Department of Chemistry, University of Torino},
keywords = {invited, hpc4ai, deephealth},
month = feb,
note = {Invited talk},
title = {Machine Learning: the treacherous journey from data to knowledge (with examples from {HPC4AI@UNITO} platform)},
url = {https://datacloud.di.unito.it/index.php/s/ffyZYYqNQpkza4F},
year = {2020},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2020_ML-chemistry.pdf}
}

M. Aldinucci, StreamFlow: cross-breeding cloud with HPC, Computability in Europe 2020 (CIE) , Invited talk, 2020.
[BibTeX] [Download PDF]

@misc{20:CIE:streamflow,
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {Computability in Europe 2020 (CIE)},
keywords = {invited, deephealth},
month = jun,
note = {Invited talk},
title = {StreamFlow: cross-breeding cloud with {HPC}},
url = {https://datacloud.di.unito.it/index.php/s/Ltqo4SmJj42wjyo},
year = {2020},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2020_streamflow-CIE.pdf}
}

Other topics

M. Aldinucci, Parallel Computing: a simple introduction, indeed an abstraction, I Lincei per la Scuola: scienze per l’innovazione digitale Virtual event: , Invited talk, March, 2022.
[BibTeX] [Download PDF]

@misc{22:lincei,
optkey = {},
author = {Marco Aldinucci},
address = {Virtual event},
title = {Parallel Computing: a simple introduction, indeed an abstraction},
howpublished = {I Lincei per la Scuola: scienze per l'innovazione digitale},
month = {March},
year = {2022},
keywords = {invited, misc},
note = {Invited talk},
url = {https://datacloud.di.unito.it/index.php/s/nmaESJrapo2NqHq},
optannote = {}
}

B. Casella, Poster Session VISIGRAPP 2022, VISIGRAPP 2022 Torino, Italy: , Session chair, 2022.
[BibTeX]

@misc{22:VISIGRAPP,
address = {Torino, Italy},
author = {Bruno Casella},
date-added = {2022-02-07 14:00:00 +0100},
howpublished = {VISIGRAPP 2022},
keywords = {invited, misc},
month = feb,
note = {Session chair},
title = {Poster Session VISIGRAPP 2022},
year = {2022},
bdsk-url-1 = {}
}

M. Aldinucci, The Joint Undertaking EuroHPC, Giornata Nazionale di Lancio dei bandi Information and Communication Technologies in Horizon 2020 Roma, Italy: , Invited talk, oct, 2019.
[BibTeX]

@misc{19:APRE:EuroHPC,
address = {Roma, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-17 14:00:00 +0100},
date-modified = {2021-03-17 14:00:00 +0100},
howpublished = {Giornata Nazionale di Lancio dei bandi Information and Communication Technologies in Horizon 2020},
keywords = {invited, misc},
month = oct,
note = {Invited talk},
title = {The Joint Undertaking EuroHPC},
year = {2019},
bdsk-url-1 = {}
}

I. Colonnelli, Deep Learning at Scale, 27th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP 2019) Pavia, Italy: IEEE, February, 2019.
[BibTeX] [Abstract] [Download PDF]

This work presents a novel approach to distributed training of deep neural networks (DNNs) that aims to overcome the issues related to mainstream approaches to data parallel training. Established techniques for data parallel training are discussed from both a parallel computing and deep learning perspective, then a different approach is presented that is meant to allow DNN training to scale while retaining good convergence properties. Moreover, an experimental implementation is presented as well as some preliminary results.

@misc{19:PDPNNT,
abstract = {This work presents a novel approach to distributed training of deep neural networks (DNNs) that aims to overcome the issues related to mainstream approaches to data parallel training. Established techniques for data parallel training are discussed from both a parallel computing and deep learning perspective, then a different approach is presented that is meant to allow DNN training to scale while retaining good convergence properties. Moreover, an experimental implementation is presented as well as some preliminary results.},
address = {Pavia, Italy},
author = {Iacopo Colonnelli},
howpublished = {27th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP 2019)},
publisher = {{IEEE}},
keywords = {misc},
month = {February},
title = {Deep Learning at Scale},
url = {https://datacloud.di.unito.it/index.php/s/nRW9M69C3AtpDoM},
year = {2019},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/nRW9M69C3AtpDoM}
}

I. Colonnelli, Accelerating spectral graph analysis through wavefronts of linear algebra operations, 27th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP 2019) Pavia, Italy: IEEE, February, 2019.
[BibTeX] [Abstract] [Download PDF]

The wavefront pattern captures the unfolding of a parallel computation in which data elements are laid out as a logical multidimensional grid and the dependency graph favours a diagonal sweep across the grid. In the emerging area of spectral graph analysis, the computing often consists in a wavefront running over a tiled matrix, involving expensive linear algebra kernels. While these applications might benefit from parallel heterogeneous platforms (multi-core with GPUs),programming wavefront applications directly with high-performance linear algebra libraries yields code that is complex to write and optimize for the specific application. We advocate a methodology based on two abstractions (linear algebra and parallel pattern-based run-time), that allows to develop portable, self-configuring, and easy-to-profile code on hybrid platforms.

@misc{19:PDPArmadillo,
abstract = {The wavefront pattern captures the unfolding of a parallel computation in which data elements are laid out as a logical multidimensional grid and the dependency graph favours a diagonal sweep across the grid. In the emerging area of spectral graph analysis, the computing often consists in a wavefront running over a tiled matrix, involving expensive linear algebra kernels. While these applications might benefit from parallel heterogeneous platforms (multi-core with GPUs),programming wavefront applications directly with high-performance linear algebra libraries yields code that is complex to write and optimize for the specific application. We advocate a methodology based on two abstractions (linear algebra and parallel pattern-based run-time), that allows to develop portable, self-configuring, and easy-to-profile code on hybrid platforms.},
address = {Pavia, Italy},
author = {Iacopo Colonnelli},
howpublished = {27th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP 2019)},
publisher = {{IEEE}},
keywords = {misc},
month = {February},
title = {Accelerating spectral graph analysis through wavefronts of linear algebra operations},
url = {https://datacloud.di.unito.it/index.php/s/zK4eSzdsdB8CfQX},
year = {2019},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/zK4eSzdsdB8CfQX}
}

M. Aldinucci, The evolution of high-performance systems: from HPC to Big Data to Deep Learning, 4th Open SmartData@PoliTO Workshop Torino, Italy: , Invited talk, feb, 2019.
[BibTeX] [Download PDF]

@misc{19:SmartData:fromHPCtoDL,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-17 15:00:00 +0100},
date-modified = {2021-03-17 15:00:00 +0100},
howpublished = {4th Open SmartData@PoliTO Workshop},
keywords = {invited, misc},
month = feb,
note = {Invited talk},
title = {The evolution of high-performance systems: from HPC to Big Data to Deep Learning},
url = {https://datacloud.di.unito.it/index.php/s/cZitMsp5GPJ2Qzf},
year = {2019},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2019_smartdata_polito.pdf}
}

M. Aldinucci, How Artificial Intelligence is shaping the Future of Health, 1st Industrial Conference on Artificial Intelligence and health Milano, Italy: , Invited talk, 2019.
[BibTeX] [Download PDF]

@misc{19:ICAIH:AIhealth,
address = {Milano, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-16 15:00:00 +0100},
date-modified = {2021-03-16 15:00:00 +0100},
howpublished = {1st Industrial Conference on Artificial Intelligence and health},
keywords = {invited, misc},
month = nov,
note = {Invited talk},
title = {How Artificial Intelligence is shaping the Future of Health},
url = {https://datacloud.di.unito.it/index.php/s/FPDCjcWwprAa7D7},
year = {2019},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2019_ICAIH.pdf}
}

M. Aldinucci, Towards DeepLearning at Scale, SPPEXA Final Symposium Dresden, Germany: , Invited talk, 2019.
[BibTeX]

@misc{19:SPPEXA:DLscale,
address = {Dresden, Germany},
author = {Marco Aldinucci},
date-added = {2021-03-17 14:00:00 +0100},
date-modified = {2021-03-17 14:00:00 +0100},
howpublished = {SPPEXA Final Symposium},
keywords = {invited, misc},
month = oct,
note = {Invited talk},
title = {Towards DeepLearning at Scale},
year = {2019},
bdsk-url-1 = {}
}

M. Aldinucci, L’infrastruttura necessaria per creare interoperabilità tra pubbliche amministrazioni, Convegno “L’amministrazione pubblica con i Big data” Turin, Italy: , Invited talk, 2019.
[BibTeX] [Download PDF]

@misc{19:conference:PublicBD,
address = {Turin, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-17 14:00:00 +0100},
date-modified = {2021-03-17 14:00:00 +0100},
howpublished = {Convegno ``L'amministrazione pubblica con i Big data''},
keywords = {invited, misc},
month = may,
note = {Invited talk},
title = {L'infrastruttura necessaria per creare interoperabilit{\`a} tra pubbliche amministrazioni},
url = {https://datacloud.di.unito.it/index.php/s/KPebr76m88jQATi},
year = {2019},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2019_Convegno_CloudPA_Aldinucci.pdf}
}

M. Aldinucci, High-Performance Computing, Istituto per la Competitività Roma, Italy: , Invited talk, 2019.
[BibTeX] [Download PDF]

@misc{19:iCom:HPC,
address = {Roma, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-17 15:00:00 +0100},
date-modified = {2021-03-17 15:00:00 +0100},
howpublished = {Istituto per la Competitivit{\`a}},
keywords = {invited, misc},
month = apr,
note = {Invited talk},
title = {High-Performance Computing},
url = {https://www.i-com.it/2019/02/02/supercomputer-hpe-computing/},
year = {2019},
bdsk-url-1 = {}
}

M. Aldinucci, L’evoluzione delle piattaforme e dei sistemi ad alte prestazioni: da HPC ai Big Data al Deep Learning, Chimica passione periodica: Big Data: Modelli predittivi, simulazione, analisi, Dipartimento di Chimica, Università di Torino Torino, Italy: , Invited talk, nov, 2018.
[BibTeX] [Download PDF]

@misc{18:ChimicaPP:fromHPCtoDL,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 12:00:00 +0100},
date-modified = {2021-03-18 12:00:00 +0100},
howpublished = {Chimica passione periodica: Big Data: Modelli predittivi, simulazione, analisi, Dipartimento di Chimica, Universit{\`a} di Torino},
keywords = {invited, misc},
month = nov,
note = {Invited talk},
title = {L'evoluzione delle piattaforme e dei sistemi ad alte prestazioni: da HPC ai Big Data al Deep Learning},
url = {https://datacloud.di.unito.it/index.php/s/sLxm6G2Ma5jj9BP},
year = {2018},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2018_chimica_passione_periodica_Aldinucci.pdf}
}

M. Aldinucci, HPC4AI, an AI-on-demand federated platform endeavour, ACM Computing Frontiers Ischia, Italy: , Invited talk, may, 2018.
[BibTeX] [Download PDF]

@misc{18:ACM:HPC4AI,
address = {Ischia, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 12:00:00 +0100},
date-modified = {2021-03-18 12:00:00 +0100},
howpublished = {ACM Computing Frontiers},
keywords = {invited, misc},
month = may,
note = {Invited talk},
title = {HPC4AI, an AI-on-demand federated platform endeavour},
url = {https://datacloud.di.unito.it/index.php/s/NWbZ2FYpeYR47T6},
year = {2018},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2018_HPC4AI_ACM_CF.pdf}
}

M. Aldinucci, Designing a heterogeneous federated data center for research, DiSIA, University of Florence Firenze, Italy: , Invited talk, mar, 2018.
[BibTeX]

@misc{18:DISIA:federated,
address = {Firenze, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 15:00:00 +0100},
date-modified = {2021-03-18 15:00:00 +0100},
howpublished = {DiSIA, University of Florence},
keywords = {invited, misc},
month = mar,
note = {Invited talk},
title = {Designing a heterogeneous federated data center for research},
year = {2018},
bdsk-url-1 = {}
}

M. Aldinucci, Le piattaforme AI-on-demand come fattore di l’innovazione nelle PMI, CITYLAB Ecosystem: i dati per le aziende, le città e le università, Nuvola Lavazza Torino Torino, Italy: , Invited talk, 2018.
[BibTeX] [Download PDF]

@misc{18:CITYLAB:AIonDemand,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 12:00:00 +0100},
date-modified = {2021-03-18 12:00:00 +0100},
howpublished = {CITYLAB Ecosystem: i dati per le aziende, le citt{\`a} e le universit{\`a}, Nuvola Lavazza Torino},
keywords = {invited, misc},
month = jun,
note = {Invited talk},
title = {Le piattaforme AI-on-demand come fattore di l'innovazione nelle PMI},
url = {https://datacloud.di.unito.it/index.php/s/Twy9zwbJB9KnBE2},
year = {2018},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2018_chimica_passione_periodica_Aldinucci.pdf}
}

M. Aldinucci, From mutexes to lock-free to atomic-less and back to (un-contended) mutex: A story in which energy efficiency is unexpectedly constantly increasing, Arm Research ltd Cambridge, UK: , Invited talk, 2018.
[BibTeX]

@misc{18:ARM:energyEfficiency,
address = {Cambridge, UK},
author = {Marco Aldinucci},
date-added = {2021-03-18 15:00:00 +0100},
date-modified = {2021-03-18 15:00:00 +0100},
howpublished = {Arm Research ltd},
keywords = {invited, misc},
month = mar,
note = {Invited talk},
title = {From mutexes to lock-free to atomic-less and back to (un-contended) mutex: A story in which energy efficiency is unexpectedly constantly increasing},
year = {2018},
bdsk-url-1 = {}
}

M. Aldinucci, Designing a heterogeneous federated data center for research, Euro-Par 2017 Santiago di Compostela, Spain: , 2017.
[BibTeX] [Download PDF]

@misc{17:EUROPAR:presentation,
address = {Santiago di Compostela, Spain},
author = {Marco Aldinucci},
date-added = {2021-03-18 15:00:00 +0100},
date-modified = {2021-03-18 15:00:00 +0100},
howpublished = {Euro-Par 2017},
keywords = {misc},
month = aug,
title = {Designing a heterogeneous federated data center for research},
url = {https://datacloud.di.unito.it/index.php/s/DyJ3A6Hgj52e33b},
year = {2017},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2017_Europar2018-presentation.pdf}
}

M. Aldinucci, OCCAM: Heterogeneous platforms mixed blessing of code optimisation, Riunione della Commissione Calcolo e Reti dell’INFN Torino, Italy: , Invited talk, 2017.
[BibTeX] [Download PDF]

@misc{17:INFN:occam,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 16:00:00 +0100},
date-modified = {2021-03-18 16:00:00 +0100},
howpublished = {Riunione della Commissione Calcolo e Reti dell'INFN},
keywords = {invited, misc},
month = sep,
note = {Invited talk},
title = {OCCAM: Heterogeneous platforms mixed blessing of code optimisation},
url = {https://agenda.infn.it/login/?next=%2Fevent%2F12983%2Ftimetable%2F%3Fview%3Dstandard},
year = {2017},
bdsk-url-1 = {}
}

M. Aldinucci, HPC come piattaforma abilitante: rischi e opportunità, Workshop of the Competence Centre on Scientific Computing of University of Torino Torino, Italy: , Invited talk, oct, 2016.
[BibTeX] [Download PDF]

@misc{16:CARLOALBERTO:dataScience,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 16:00:00 +0100},
date-modified = {2021-03-18 16:00:00 +0100},
howpublished = {Workshop of the Competence Centre on Scientific Computing of University of Torino},
keywords = {invited, misc},
month = oct,
note = {Invited talk},
title = {HPC come piattaforma abilitante: rischi e opportunit{\`a}},
url = {https://datacloud.di.unito.it/index.php/s/6qgoQoSXaZpMQq4},
year = {2016},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2016_C3S_Aldinucci.pdf}
}

M. Aldinucci, Atomic operations considered hARMful, ARM Research Summary Cambridge, UK: , sep, 2016.
[BibTeX] [Download PDF]

@misc{16:ARM:atomic,
address = {Cambridge, UK},
author = {Marco Aldinucci},
date-added = {2021-03-18 16:00:00 +0100},
date-modified = {2021-03-18 16:00:00 +0100},
howpublished = {ARM Research Summary},
keywords = {misc},
month = sep,
title = {Atomic operations considered hARMful},
url = {https://datacloud.di.unito.it/index.php/s/mBoWAmMzj8nYrbe},
year = {2016},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2016_C3S_Aldinucci.pdf}
}

M. Aldinucci, I supercomputer venuti dal futuro: Exascale Computing, International Pint of Science, Officine ferroviarie Torino, Italy: , Invited talk, may, 2016.
[BibTeX]

@misc{16:PintOfScience:exascale,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 16:00:00 +0100},
date-modified = {2021-03-18 16:00:00 +0100},
howpublished = {International Pint of Science, Officine ferroviarie},
keywords = {invited, misc},
month = may,
note = {Invited talk},
title = {I supercomputer venuti dal futuro: Exascale Computing},
year = {2016},
bdsk-url-1 = {}
}

M. Aldinucci, Composition and Compartmentalisation As Enabling Features for Data-Centric, Extreme Scale Applications: An MPI-X Approach, SIAM Conference on Parallel Processing for Scientific Computing Paris, France: , apr, 2016.
[BibTeX] [Download PDF]

@misc{16:SIAM:composition,
address = {Paris, France},
author = {Marco Aldinucci},
date-added = {2021-03-18 16:00:00 +0100},
date-modified = {2021-03-18 16:00:00 +0100},
howpublished = {SIAM Conference on Parallel Processing for Scientific Computing},
keywords = {misc},
month = apr,
title = {Composition and Compartmentalisation As Enabling Features for Data-Centric, Extreme Scale Applications: An MPI-X Approach},
url = {https://datacloud.di.unito.it/index.php/s/cWne6zEWypepo7H},
year = {2016},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2016_C3S_Aldinucci.pdf}
}

M. Aldinucci, , FastData@UNITO opening Torino, Italy: , Invited talk, mar, 2016.
[BibTeX]

@misc{16:UNITO:fastdata,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 16:00:00 +0100},
date-modified = {2021-03-18 16:00:00 +0100},
howpublished = {FastData@UNITO opening},
keywords = {invited, misc},
month = mar,
note = {Invited talk},
year = {2016},
bdsk-url-1 = {}
}

M. Aldinucci, An Overview of FastFlow: Combining Pattern-Level Abstraction and Efficiency in GPGPUs, GPU Technology Conference (GTC 2014) San Jose, CA, USA: , mar, 2014.
[BibTeX] [Download PDF]

@misc{14:GTC:overview,
address = {San Jose, CA, USA},
author = {Marco Aldinucci},
date-added = {2021-03-18 16:00:00 +0100},
date-modified = {2021-03-18 16:00:00 +0100},
howpublished = {GPU Technology Conference (GTC 2014)},
keywords = {misc},
month = mar,
title = {An Overview of FastFlow: Combining Pattern-Level Abstraction and Efficiency in GPGPUs},
url = {https://datacloud.di.unito.it/index.php/s/sRYnknEHn3Qkj5b},
year = {2014},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2016_C3S_Aldinucci.pdf}
}

M. Aldinucci, FastFlow: Combining Pattern-Level Abstraction and Efficiency in GPGPUs, GPU Technology Conference (GTC 2014) San Jose, CA, USA: , mar, 2014.
[BibTeX] [Download PDF]

@misc{14:GTC:fastflow,
address = {San Jose, CA, USA},
author = {Marco Aldinucci},
date-added = {2021-03-18 16:00:00 +0100},
date-modified = {2021-03-18 16:00:00 +0100},
howpublished = {GPU Technology Conference (GTC 2014)},
keywords = {misc},
month = mar,
title = {FastFlow: Combining Pattern-Level Abstraction and Efficiency in GPGPUs},
url = {https://datacloud.di.unito.it/index.php/s/ibCGJc7tJyASMbN},
year = {2014},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2014_S4585-Marco-Aldinucci.pdf}
}

M. Aldinucci, Turning Big data into knowledge: systems biology droplets in the cloud, Cloud and Science seminar at “the Ramon Areces Foundation” Madrid, Spain: , invited talk, mar, 2013.
[BibTeX] [Download PDF]

@misc{13:RAF:cloud,
address = {Madrid, Spain},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {Cloud and Science seminar at ``the Ramon Areces Foundation''},
keywords = {invited, misc},
month = mar,
note = {invited talk},
title = {Turning Big data into knowledge: systems biology droplets in the cloud},
url = {https://datacloud.di.unito.it/index.php/s/oBPqA5DeyCbj5do},
year = {2013},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2013_CWC_FRA.pdf}
}

M. Aldinucci, A Parallel Edge Preserving Algorithm for Salt and Pepper Image Denoising, IEEE Intl. Conference on Image Processing Theory, Tools and Applications (IPTA) Istanbul, Turkey: , oct, 2012.
[BibTeX] [Download PDF]

@misc{12:IEEE:denoising,
address = {Istanbul, Turkey},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {IEEE Intl. Conference on Image Processing Theory, Tools and Applications (IPTA)},
keywords = {misc},
month = oct,
title = {A Parallel Edge Preserving Algorithm for Salt and Pepper Image Denoising},
url = {https://datacloud.di.unito.it/index.php/s/JQe8RDp9jp4iymF},
year = {2012},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2012_IPTA_Aldinucci.pdf}
}

M. Aldinucci, Turning Big data into knowledge: Techniques and Tools for Parallel Computing on Online Data Streams in Systems Biology and Epidemiology, Bio-IT World Europe Vienna, Austria: , invited talk, oct, 2012.
[BibTeX] [Download PDF]

@misc{12:BIOIT:bigdata,
address = {Vienna, Austria},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {Bio-IT World Europe},
keywords = {invited, misc},
month = oct,
note = {invited talk},
title = {Turning Big data into knowledge: Techniques and Tools for Parallel Computing on Online Data Streams in Systems Biology and Epidemiology},
url = {https://datacloud.di.unito.it/index.php/s/BjLQyiDofH6gB3d},
year = {2012},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2012_BioIT_CWC.pdf}
}

M. Aldinucci, FastFlow: high-level programming patterns with non-blocking lock-free run-time support, UPMARC Workshop on Task-Based Parallel Programming Uppsala, Sweden: , invited talk, sep, 2012.
[BibTeX] [Download PDF]

@misc{12:UPMARC:fastflow,
address = {Uppsala, Sweden},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {UPMARC Workshop on Task-Based Parallel Programming},
keywords = {invited, misc},
month = sep,
note = {invited talk},
title = {FastFlow: high-level programming patterns with non-blocking lock-free run-time support},
url = {https://datacloud.di.unito.it/index.php/s/BWGrrsTWidMtcCn},
year = {2012},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2012_UPMARC.pdf}
}

M. Aldinucci, Pattern-based Parallel Edge Preserving Algorithm for Salt-and-Pepper Image Denoising, HPC Advisory Council Switzerland Conference Lugano, Switzerland: , invited talk, mar, 2012.
[BibTeX] [Download PDF]

@misc{12:HPCAC:denoising,
address = {Lugano, Switzerland},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {HPC Advisory Council Switzerland Conference},
keywords = {invited, misc},
month = mar,
note = {invited talk},
title = {Pattern-based Parallel Edge Preserving Algorithm for Salt-and-Pepper Image Denoising},
url = {https://datacloud.di.unito.it/index.php/s/BCm7w9S86yDNJb2},
year = {2012},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2012_HPCAC_UNITO-Aldinucci.pdf}
}

M. Aldinucci, Performance and productivity in the multi-core era: challenges in software engineering and formal methods, IMT Lucca, Italy: , invited talk, nov, 2011.
[BibTeX] [Download PDF]

@misc{11:IMT:muticore,
address = {Lucca, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {IMT},
keywords = {invited, misc},
month = nov,
note = {invited talk},
title = {Performance and productivity in the multi-core era: challenges in software engineering and formal methods},
url = {https://datacloud.di.unito.it/index.php/s/Ppok8Cno7enc6QT},
year = {2011},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2011_IMT_ff.pdf}
}

M. Aldinucci, The camera that makes you beauty (with a novel real-time video-denoiser algorithm), TOSM expo Torino, Italy: , invited talk, nov, 2011.
[BibTeX]

@misc{11:TOSM:denoiser,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {TOSM expo},
keywords = {invited, misc},
month = nov,
note = {invited talk},
title = {The camera that makes you beauty (with a novel real-time video-denoiser algorithm)},
year = {2011},
bdsk-url-1 = {}
}

M. Aldinucci, FastFlow: Performance and Productivity in the Exascale Era, IBM Research, Exascale laboratory Dublin, Ireland: , invited talk, oct, 2011.
[BibTeX]

@misc{11:IBM:fastflow,
address = {Dublin, Ireland},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {IBM Research, Exascale laboratory},
keywords = {invited, misc},
month = oct,
note = {invited talk},
title = {FastFlow: Performance and Productivity in the Exascale Era},
year = {2011},
bdsk-url-1 = {}
}

M. Aldinucci, FastFlow: Performance and Productivity in the Multicore Era, Formal Methods for Components and Objects (FMCO) Torino, Italy: , invited talk, oct, 2011.
[BibTeX] [Download PDF]

@misc{11:FMCO:fastflow,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {Formal Methods for Components and Objects (FMCO)},
keywords = {invited, misc},
month = oct,
note = {invited talk},
title = {FastFlow: Performance and Productivity in the Multicore Era},
url = {https://datacloud.di.unito.it/index.php/s/3opjTzm6XRcjYEs},
year = {2011},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2011_FMCO_ff.pdf}
}

M. Aldinucci, MyMed: Un social network geosensibile per reti fisse e mobili basato sul paradigma Peer-to-Peer, La notte dei ricercatori Torino, Italy: , invited talk, sep, 2011.
[BibTeX] [Download PDF]

@misc{11:notteRicercatori:myMed,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {La notte dei ricercatori},
keywords = {invited, misc},
month = sep,
note = {invited talk},
title = {MyMed: Un social network geosensibile per reti fisse e mobili basato sul paradigma Peer-to-Peer},
url = {https://datacloud.di.unito.it/index.php/s/QjcSysM93X9DCDn},
year = {2011},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2011_09_Aldinucci-notte-Torino.pdf}
}

M. Aldinucci, On Parallelizing On-Line Statistics for Stochastic Biological Simulations, Workshop on High Performance Bioinformatics and Biomedicine (HiBB) Bordeaux, France: , aug, 2011.
[BibTeX] [Download PDF]

@misc{11:HiBB:simulation,
address = {Bordeaux, France},
author = {Marco Aldinucci},
date-added = {2021-03-18 18:00:00 +0100},
date-modified = {2021-03-18 18:00:00 +0100},
howpublished = {Workshop on High Performance Bioinformatics and Biomedicine (HiBB)},
keywords = {misc},
month = aug,
title = {On Parallelizing On-Line Statistics for Stochastic Biological Simulations},
url = {https://datacloud.di.unito.it/index.php/s/3ijP5zx2ktT6Xab},
year = {2011},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2011_cwc_clustering_hibb_talk.pdf}
}

M. Aldinucci, Accelerating code on multi-cores with FastFlow, Euro-Par 2011 Bordeaux, France: , aug, 2011.
[BibTeX] [Download PDF]

@misc{11:EUROPAR:fastflow,
address = {Bordeaux, France},
author = {Marco Aldinucci},
date-added = {2021-03-18 18:00:00 +0100},
date-modified = {2021-03-18 18:00:00 +0100},
howpublished = {Euro-Par 2011},
keywords = {misc},
month = aug,
title = {Accelerating code on multi-cores with FastFlow},
url = {https://datacloud.di.unito.it/index.php/s/Z4RtA9QoxCm829j},
year = {2011},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2011-sep-ff_acceleator_europar.pdf}
}

M. Aldinucci, High-level parallel programming: (few) ideas for challenges in formal methods, COST Action IC701 workshop Limerick, Republic of Ireland: , invited talk, jun, 2011.
[BibTeX] [Download PDF]

@misc{11:COST:parallel,
address = {Limerick, Republic of Ireland},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {COST Action IC701 workshop},
keywords = {invited, misc},
month = jun,
note = {invited talk},
title = {High-level parallel programming: (few) ideas for challenges in formal methods},
url = {https://datacloud.di.unito.it/index.php/s/XfikGmd2e6pwfaM},
year = {2011},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2011_COST_IC701_ff.pdf}
}

M. Aldinucci, On Designing Multicore-Aware Simulators for Biological Systems, IEEE Euromicro PDP 2011: Parallel Distributed and network-based Processing Ayia Napa, Ciprus: , feb, 2011.
[BibTeX] [Download PDF]

@misc{11:IEEE:simulator,
address = {Ayia Napa, Ciprus},
author = {Marco Aldinucci},
date-added = {2021-03-18 18:00:00 +0100},
date-modified = {2021-03-18 18:00:00 +0100},
howpublished = {IEEE Euromicro PDP 2011: Parallel Distributed and network-based Processing},
keywords = {misc},
month = feb,
title = {On Designing Multicore-Aware Simulators for Biological Systems},
url = {https://datacloud.di.unito.it/index.php/s/Anb4Gdj8p9yq4C2},
year = {2011},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2011_CWCsim_PDP.pdf}
}

M. Aldinucci, FastFlow: a pattern-based programming framework for multicores, seminar 10191 Wadern, Germania: , invited talk, may, 2010.
[BibTeX] [Download PDF]

@misc{10:SCHOSS:fastflow,
address = {Wadern, Germania},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {seminar 10191},
keywords = {invited, misc},
month = may,
note = {invited talk},
title = {FastFlow: a pattern-based programming framework for multicores},
url = {https://datacloud.di.unito.it/index.php/s/fMpTjXat97kp2Hp},
year = {2010},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2010_FastFlow_PDP.pdf}
}

M. Aldinucci, Efficient Smith-Waterman on multi-core with FastFlow, PDP 2010: Parallel Distributed and network-based Processing Pisa, Italy: , feb, 2010.
[BibTeX] [Download PDF]

@misc{10:PDP:fastflow,
address = {Pisa, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 18:00:00 +0100},
date-modified = {2021-03-18 18:00:00 +0100},
howpublished = {PDP 2010: Parallel Distributed and network-based Processing},
keywords = {misc},
month = feb,
title = {Efficient Smith-Waterman on multi-core with FastFlow},
url = {https://datacloud.di.unito.it/index.php/s/2RM8k6j3QGo5R7C},
year = {2010},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2010_FastFlow_PDP.pdf}
}

M. Aldinucci, Efficient streaming applications on multi-core with FastFlow: the biosequence alignment test-bed, ParCo 2009 Lyon, France: , sep, 2009.
[BibTeX] [Download PDF]

@misc{09:PARCO:fastflow,
address = {Lyon, France},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {ParCo 2009},
keywords = {misc},
month = sep,
title = {Efficient streaming applications on multi-core with FastFlow: the biosequence alignment test-bed},
url = {https://datacloud.di.unito.it/index.php/s/Aa26ZyrfSB7ZWzj},
year = {2009},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2009_FastFlow_ParCo.pdf}
}

M. Aldinucci, STKM on SCA: A unified framework with components, workflows and algorithmic skeletons, EuroPar 2009 Delft, The Netherlands: , sep, 2009.
[BibTeX] [Download PDF]

@misc{09:EUROPAR:stkm,
address = {Delft, The Netherlands},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {EuroPar 2009},
keywords = {misc},
month = sep,
title = {STKM on SCA: A unified framework with components, workflows and algorithmic skeletons},
url = {https://datacloud.di.unito.it/index.php/s/KDyBfqmt9HXXwKT},
year = {2009},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2009_STKM_EuroPar.pdf}
}

M. Aldinucci, Towards Hierarchical Management of Autonomic Components: a Case Study, Euromicro PDP 2009: Parallel Distributed and network-based Processing Weimar, Germany: , feb, 2009.
[BibTeX] [Download PDF]

@misc{09:PDP:hierarchical,
address = {Weimar, Germany},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {Euromicro PDP 2009: Parallel Distributed and network-based Processing},
keywords = {misc},
month = feb,
title = {Towards Hierarchical Management of Autonomic Components: a Case Study},
url = {https://datacloud.di.unito.it/index.php/s/KTn3dGwQwbe3bMc},
year = {2009},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2009_hierarchic_PDP.pdf}
}

M. Aldinucci, Towards a Formal Semantics for Autonomic Components, CoreGRID Symposium Las Palmas de Gran Canaria, Canary Island, Spain: , aug, 2008.
[BibTeX]

@misc{08:COREGRID:semantics,
address = {Las Palmas de Gran Canaria, Canary Island, Spain},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {CoreGRID Symposium},
keywords = {misc},
month = aug,
title = {Towards a Formal Semantics for Autonomic Components},
year = {2008},
bdsk-url-1 = {}
}

M. Aldinucci, Autonomic components in GCM, CoreGRID Scientific Advisory Board Amsterdam, The Netherland: , invited talk, may, 2008.
[BibTeX] [Download PDF]

@misc{08:COREGRID:autonomic,
address = {Amsterdam, The Netherland},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {CoreGRID Scientific Advisory Board},
keywords = {invited, misc},
month = may,
note = {invited talk},
title = {Autonomic components in GCM},
url = {https://datacloud.di.unito.it/index.php/s/22S3LwsTjbkG3w3},
year = {2008},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2008_CGSymph_compsem.pdf}
}

M. Aldinucci, VirtuaLinux: Una soluzione open source per il clustering HPC, Net & System Security Pisa, Italia: , invited talk in italian, nov, 2007.
[BibTeX] [Download PDF]

@misc{07:NSS:virtualLinux,
address = {Pisa, Italia},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {Net & System Security},
keywords = {invited, misc},
month = nov,
note = {invited talk in italian},
title = {VirtuaLinux: Una soluzione open source per il clustering HPC},
url = {https://datacloud.di.unito.it/index.php/s/4SWFntywGgDtoxx},
year = {2007},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2007_Virtualinux_NSS.pdf}
}

M. Aldinucci, Behavioural skeletons for component autonomic management on grids, CoreGRID Workshop on Grid Programming Model, Grid and P2P Systems Architecture, Grid Systems, Tools and Environments Heraklion, Crete, Greece: , jun, 2007.
[BibTeX] [Download PDF]

@misc{07:COREGRID:skeletons,
address = {Heraklion, Crete, Greece},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {CoreGRID Workshop on Grid Programming Model, Grid and P2P Systems Architecture, Grid Systems, Tools and Environments},
keywords = {misc},
month = jun,
title = {Behavioural skeletons for component autonomic management on grids},
url = {https://datacloud.di.unito.it/index.php/s/ZpidDEMJo8Pc4nB},
year = {2007},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2007_BeSke_Crete.pdf}
}

M. Aldinucci, Taming the grid through dynamic adaptation: results and open problems, Last Advances in Computer Science San Cristobal de la Laguna, Tenerife, Canarian Islands, Spain: , invited talk, nov, 2006.
[BibTeX] [Download PDF]

@misc{06:LACS:adptation,
address = {San Cristobal de la Laguna, Tenerife, Canarian Islands, Spain},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {Last Advances in Computer Science},
keywords = {invited, misc},
month = nov,
note = {invited talk},
title = {Taming the grid through dynamic adaptation: results and open problems},
url = {https://datacloud.di.unito.it/index.php/s/2i8tDjMYEYnZeFo},
year = {2006},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2006_LaLaguna_UAI.pdf}
}

M. Aldinucci, Fault-tolerant data sharing for high-level grid programming: a hierarchical storage architecture, CoreGRID Integration Workshop Krakow, Poland: , oct, 2006.
[BibTeX] [Download PDF]

@misc{06:COREGRID:storage,
address = {Krakow, Poland},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {CoreGRID Integration Workshop},
keywords = {misc},
month = oct,
title = {Fault-tolerant data sharing for high-level grid programming: a hierarchical storage architecture},
url = {https://datacloud.di.unito.it/index.php/s/iibrkEtMREHiexA},
year = {2006},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2006_IW_assistjuxmem.pdf}
}

M. Aldinucci, Autonomic QoS in ASSIST Grid-aware components, IEEE Euromicro PDP 2006: Parallel Distributed and network-based Processing Montbéliard, France: , feb, 2006.
[BibTeX] [Download PDF]

@misc{06:IEEE:qos,
address = {Montb{\'e}liard, France},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {IEEE Euromicro PDP 2006: Parallel Distributed and network-based Processing},
keywords = {misc},
month = feb,
title = {Autonomic QoS in ASSIST Grid-aware components},
url = {https://datacloud.di.unito.it/index.php/s/bcCddKkiF9KsDWM},
year = {2006},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2006_assist_QoS_pdp_talk.pdf}
}

M. Aldinucci, Building Interoperable Grid-aware ASSIST Applications via Web Services, ParCo 2005 Malaga, Spain: , sep, 2005.
[BibTeX] [Download PDF]

@misc{05:PARCO:assist,
address = {Malaga, Spain},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {ParCo 2005},
keywords = {misc},
month = sep,
title = {Building Interoperable Grid-aware ASSIST Applications via Web Services},
url = {https://datacloud.di.unito.it/index.php/s/6rbd7RWE6fbMbsi},
year = {2005},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2005_WS_parco_talk.pdf}
}

M. Aldinucci, Towards a distributed scalable data service for the Grid, ParCo 2005 Malaga, Spain: , sep, 2005.
[BibTeX] [Download PDF]

@misc{05:PARCO:dataService,
address = {Malaga, Spain},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {ParCo 2005},
keywords = {misc},
month = sep,
title = {Towards a distributed scalable data service for the Grid},
url = {https://datacloud.di.unito.it/index.php/s/zQMGHBAWRLdKsbH},
year = {2005},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2005_ADHOC_parco_talk.pdf}
}

M. Aldinucci, Dynamic reconfiguration of grid-aware applications in ASSIST, Euro-Par 2005 Lisbon, Portugal: , sep, 2005.
[BibTeX] [Download PDF]

@misc{05:EUROPAR:assist,
address = {Lisbon, Portugal},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {Euro-Par 2005},
keywords = {misc},
month = sep,
title = {Dynamic reconfiguration of grid-aware applications in ASSIST},
url = {https://datacloud.di.unito.it/index.php/s/nPESNMGWirAYEFB},
year = {2005},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2005_assist_dyn_europar_talk.pdf}
}

M. Aldinucci, Rendering Grid Heterogeneity Harmless, Seminar 04451 – Future Generation Grids Dagstuhl, Germany: , invited talk, nov, 2004.
[BibTeX] [Download PDF]

@misc{04:FGG:heterogeneity,
address = {Dagstuhl, Germany},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {Seminar 04451 -- Future Generation Grids},
keywords = {invited, misc},
month = nov,
note = {invited talk},
title = {Rendering Grid Heterogeneity Harmless},
url = {https://datacloud.di.unito.it/index.php/s/ofZiZrd8YpWgqZD},
year = {2004},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2004_dagstuhl_talk.pdf}
}

M. Aldinucci, Accelerating Apache farms through ad-HOC distributed scalable object repository, Euro-Par 2004 Pisa, Italy: , sep, 2004.
[BibTeX] [Download PDF]

@misc{04:EUROPAR:apache,
address = {Pisa, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {Euro-Par 2004},
keywords = {misc},
month = sep,
title = {Accelerating Apache farms through ad-HOC distributed scalable object repository},
url = {https://datacloud.di.unito.it/index.php/s/zLXTRRQb2rJNyDy},
year = {2004},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2004_adhoc_europar_talk.pdf}
}

M. Aldinucci, Optimization techniques for implementing parallel skeletons in Grid, CMPP 2004 (in conjunction with MPC 04) Stirling, Scotland: , 2004.
[BibTeX] [Download PDF]

@misc{04:CMPP:skeletons,
address = {Stirling, Scotland},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {CMPP 2004 (in conjunction with MPC 04)},
keywords = {misc},
month = jul,
title = {Optimization techniques for implementing parallel skeletons in Grid},
url = {https://datacloud.di.unito.it/index.php/s/MozKWbWPCxko2CX},
year = {2004},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2004_lithium_cmpp_talk.pdf}
}

M. Aldinucci, An operational semantics for skeletons, ParCo 2003 Dresden, Germany: , sep, 2003.
[BibTeX] [Download PDF]

@misc{03:PARCO:skeletons,
address = {Dresden, Germany},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {ParCo 2003},
keywords = {misc},
month = sep,
title = {An operational semantics for skeletons},
url = {https://datacloud.di.unito.it/index.php/s/xDnFpT6RM9LHAEd},
year = {2003},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2003_semantics_parco_talk.pdf}
}

M. Aldinucci, A framework for experimenting with structured parallel programming environment design, ParCo 2003 Dresden, Germany: , sep, 2003.
[BibTeX] [Download PDF]

@misc{03:PARCO:framework,
address = {Dresden, Germany},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {ParCo 2003},
keywords = {misc},
month = sep,
title = {A framework for experimenting with structured parallel programming environment design},
url = {https://datacloud.di.unito.it/index.php/s/FkGk3giN8MiGMwo},
year = {2003},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2003_ASSIST_parco_talk.pdf}
}

M. Aldinucci, ASSIST demo: a high level, high performance, portable, structured parallel programming environment at work, 9th Intl Euro-Par 2003: Parallel and Distributed Computing Austria: , aug, 2003.
[BibTeX] [Download PDF]

@misc{03:EUROPAR:assist,
address = {Austria},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {9th Intl Euro-Par 2003: Parallel and Distributed Computing},
keywords = {misc},
month = aug,
title = {ASSIST demo: a high level, high performance, portable, structured parallel programming environment at work},
url = {https://datacloud.di.unito.it/index.php/s/fmEcpJDGD4iitia},
year = {2003},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2003_assist_poster_europar_a4.pdf}
}

M. Aldinucci, eskimo: experimenting skeletons on the shared address model, HLPP 2003 Paris, France: , jun, 2003.
[BibTeX] [Download PDF]

@misc{03:HLPP:eskimo,
address = {Paris, France},
author = {Marco Aldinucci},
date-added = {2021-03-18 22:00:00 +0100},
date-modified = {2021-03-18 22:00:00 +0100},
howpublished = {HLPP 2003},
keywords = {misc},
month = jun,
title = {eskimo: experimenting skeletons on the shared address model},
url = {https://datacloud.di.unito.it/index.php/s/noo2N7HBKWm2nMH},
year = {2003},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2003_eskimo_hlpp_talk.pdf}
}

M. Aldinucci, The META transformation tool for skeleton-based language, CMPP 2000 Ponte de Lima, Portugal: , jul, 2000.
[BibTeX] [Download PDF]

@misc{00:CMPP:META,
address = {Ponte de Lima, Portugal},
author = {Marco Aldinucci},
date-added = {2021-03-18 22:00:00 +0100},
date-modified = {2021-03-18 22:00:00 +0100},
howpublished = {CMPP 2000},
keywords = {misc},
month = jul,
title = {The META transformation tool for skeleton-based language},
url = {https://datacloud.di.unito.it/index.php/s/e9L5fWDo9q5fmw4},
year = {2000},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2000_meta_cmpp_talk.pdf}
}

M. Aldinucci, E. Baralis, V. Cardellini, I. Colonnelli, M. Danelutto, S. Decherchi, G. D. Modica, L. Ferrucci, M. Gribaudo, F. Iannone, M. Lapegna, D. Medić, G. Muscianisi, F. Righetti, E. Sciacca, N. Tonellotto, M. Tortonesi, P. Trunfio, and T. Vardanega, A Systematic Mapping Study of Italian Research on Workflows, 18th Workshop on Workflows in Support of Large-Scale Science (WORKS 2023) Denver, CO, Usa: , November, 2023.
[BibTeX] [Abstract] [Download PDF]

An entire ecosystem of methodologies and tools revolves around scientific workflow management. They cover crucial non-functional requirements that standard workflow models fail to target, such as interactive execution, energy efficiency, performance portability, Big Data management, and intelligent orchestration in the Computing Continuum. Characterizing and monitoring this ecosystem is crucial to developing an informed view of current and future research directions. This work conducts a systematic mapping study of the Italian workflow research community, analyzing 25 tools and 10 applications from several scientific domains in the context of the “National Research Centre for HPC, Big Data, and Quantum Computing” (ICSC). The study aims to outline the main current research directions and determine how they address the critical needs of modern scientific applications. The findings highlight a variegated research ecosystem of tools, with a prominent interest in advanced workflow orchestration and still immature but promising efforts toward energy efficiency.

@misc{23:sc:works,
address = {Denver, CO, Usa},
author = {Marco Aldinucci and
Elena Baralis and
Valeria Cardellini and
Iacopo Colonnelli and
Marco Danelutto and
Sergio Decherchi and
Giuseppe Di Modica and
Luca Ferrucci and
Marco Gribaudo and
Francesco Iannone and
Marco Lapegna and
Doriana Medi\'{c} and
Giuseppa Muscianisi and
Francesca Righetti and
Eva Sciacca and
Nicola Tonellotto and
Mauro Tortonesi and
Paolo Trunfio and
Tullio Vardanega},
abstract = {An entire ecosystem of methodologies and tools revolves around scientific workflow management. They cover crucial non-functional requirements that standard workflow models fail to target, such as interactive execution, energy efficiency, performance portability, Big Data management, and intelligent orchestration in the Computing Continuum. Characterizing and monitoring this ecosystem is crucial to developing an informed view of current and future research directions. This work conducts a systematic mapping study of the Italian workflow research community, analyzing 25 tools and 10 applications from several scientific domains in the context of the ``National Research Centre for HPC, Big Data, and Quantum Computing'' (ICSC). The study aims to outline the main current research directions and determine how they address the critical needs of modern scientific applications. The findings highlight a variegated research ecosystem of tools, with a prominent interest in advanced workflow orchestration and still immature but promising efforts toward energy efficiency.},
howpublished = {18th Workshop on Workflows in Support of Large-Scale Science (WORKS 2023)},
keywords = {icsc},
title = {A Systematic Mapping Study of Italian Research on Workflows},
year = {2023},
month = {November},
url = {https://datacloud.di.unito.it/index.php/s/2kgooG43pGCykji}
}

@misc{23:sc:WCIBoF,
address = {Denver, CO, Usa},
author = {Alberto Scionti and Iacopo Colonnelli},
howpublished = {Workflows Community: Modern Workflows for Continuum and Cross-Facility Computing},
keywords = {across, streamflow},
title = {Orchestrating Multi-Domain Workflows: The {ACROSS} Approach},
year = {2023},
month = {November},
url = {https://datacloud.di.unito.it/index.php/s/rJXcDBK4mLmS8yz}
}

I. Colonnelli, ACROSS: HPC Big Data Artificial Intelligence Cross Stack Platform Towards Exascale, LN HPC-KTT Assemblea Nazionale 2023 Pisa, Italy: , October, 2023.
[BibTeX] [Download PDF]

@misc{23:AssembleaHPC-KTT,
address = {Pisa, Italy},
author = {Iacopo Colonnelli},
keywords = {across, streamflow},
month = {October},
howpublished = {LN HPC-KTT Assemblea Nazionale 2023},
title = {{ACROSS}: {HPC} Big Data Artificial Intelligence Cross Stack Platform Towards Exascale},
url = {https://datacloud.di.unito.it/index.php/s/aK7es8BgFeWorjD},
year = {2023}
}

I. Colonnelli, D. Medić, B. Cantalupo, and M. Aldinucci, Università degli Studi di Torino: Alpha parallel research group, HaMMon Kick-Off meeting Bologna, Italy: , October, 2023.
[BibTeX] [Download PDF]

@misc{23:HaMMonProject,
address = {Bologna, Italy},
author = {Iacopo Colonnelli and
Doriana Medi\'{c} and
Barbara Cantalupo and
Marco Aldinucci},
keywords = {icsc, streamflow},
month = {October},
howpublished = {HaMMon Kick-Off meeting},
title = {Universit\`{a} degli Studi di Torino: Alpha parallel research group},
url = {https://datacloud.di.unito.it/index.php/s/cmgy9BZ3nwCR2QJ},
year = {2023}
}

I. Colonnelli, Workflow models for heterogeneous distributed systems, 2nd Italian Conference on Big Data and Data Science (ITADATA 2023) Napoli, Italy: , Best PhD Thesis Award, September, 2023.
[BibTeX] [Download PDF]

@misc{23:ITADATABestPhDThesis,
address = {Napoli, Italy},
author = {Iacopo Colonnelli},
keywords = {streamflow, jupyter-workflow},
month = {September},
howpublished = {2nd Italian Conference on Big Data and Data Science (ITADATA 2023)},
title = {Workflow models for heterogeneous distributed systems},
url = {https://datacloud.di.unito.it/index.php/s/6RqcaJ4djqFNDC8},
note = {Best PhD Thesis Award},
year = {2023}
}

This paper presents a comprehensive benchmarking study of various Federated Learning (FL) frameworks applied to the task of Medical Image Classification. The research specifically addresses the often neglected and complex aspects of scalability and usability in off-the-shelf FL frameworks. Through experimental validation using real case deployments, we provide empirical evidence of the performance and practical relevance of open source FL frameworks. Our findings contribute valuable insights for anyone interested in deploying a FL system, with a particular focus on the healthcare domain—an increasingly attractive field for FL applications.

@misc{23:iciap:benchmed,
address = {Udine, Italy},
author = {Samuele Fonio},
abstract = {This paper presents a comprehensive benchmarking study of various Federated Learning (FL) frameworks applied to the task of Medical Image Classification. The research specifically addresses the often neglected and complex aspects of scalability and usability in off-the-shelf FL frameworks. Through experimental validation using real case deployments, we provide empirical evidence of the performance and practical relevance of open source FL frameworks. Our findings contribute valuable insights for anyone interested in deploying a FL system, with a particular focus on the healthcare domain—an increasingly attractive field for FL applications.},
howpublished = {Image Analysis and Processing - ICIAP 2023 - 22th International Conference - FedMed},
keywords = {icsc, eupilot},
title = {Benchmarking Federated Learning Frameworks for Medical Imaging Tasks},
year = {2023},
month = {sep},
url = {https://datacloud.di.unito.it/index.php/s/sR7YeTGgfH4DtCR}
}

Federated Learning (FL) is a widespread Machine Learning paradigm handling distributed Big Data. In this work, we demonstrate that different FL frameworks expose different scaling performances despite adopting the same technologies, highlighting the need for a more comprehensive study on the topic.

@misc{23:itadata:fl_scaling,
address = {Naples, Italy},
author = {Gianluca Mittone and Samuele Fonio},
abstract = {Federated Learning (FL) is a widespread Machine Learning paradigm handling distributed Big Data. In this work, we demonstrate that different FL frameworks expose different scaling performances despite adopting the same technologies, highlighting the need for a more comprehensive study on the topic.},
keywords = {invited, eupilot, icsc},
month = {September},
howpublished = {2nd Italian Conference on Big Data and Data Science (ITADATA 2023)},
title = {Benchmarking Federated Learning Scalability},
note = {},
year = {2023},
url = {https://datacloud.di.unito.it/index.php/s/QZGxC4X3s5LG5oT}
}

Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs); this allowed its development as DNNs proliferated but neglected those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only support DNNs reinforces this problem. To address the lack of non-DNN-based FL solutions, we propose MAFL (Model-Agnostic Federated Learning). MAFL merges a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel® OpenFL. MAFL is the first FL system not tied to any machine learning model, allowing exploration of FL beyond DNNs. We test MAFL from multiple points of view, assessing its correctness, flexibility, and scaling properties up to 64 nodes of an HPC cluster. We also show how we optimised OpenFL achieving a 5.5x speedup over a standard FL scenario. MAFL is compatible with x86-64, ARM-v8, Power and RISC-V.

@misc{23:europar:phdtalk,
address = {Limassol, Cyprus},
author = {Gianluca Mittone and Walter Riviera and Iacopo Colonnelli and Robert Birke and Marco Aldinucci},
abstract = {Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs); this allowed its development as DNNs proliferated but neglected those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only support DNNs reinforces this problem. To address the lack of non-DNN-based FL solutions, we propose MAFL (Model-Agnostic Federated Learning). MAFL merges a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel® OpenFL. MAFL is the first FL system not tied to any machine learning model, allowing exploration of FL beyond DNNs. We test MAFL from multiple points of view, assessing its correctness, flexibility, and scaling properties up to 64 nodes of an HPC cluster. We also show how we optimised OpenFL achieving a 5.5x speedup over a standard FL scenario. MAFL is compatible with x86-64, ARM-v8, Power and RISC-V.},
keywords = {invited, eupilot, icsc},
month = {September},
howpublished = {29th International European Conference on Parallel and Distributed Computing (Euro-Par '23)},
title = {Model-Agnostic Federated Learning},
note = {},
year = {2023},
url = {https://datacloud.di.unito.it/index.php/s/rBxPq7DqNTWxFk8}
}

Federated Learning (FL) is experiencing a substantial research interest, with many frameworks being developed to allow practitioners to build federations easily and quickly. Most of these efforts do not consider two main aspects that are key to Machine Learning (ML) software: customizability and performance. This research addresses these issues by implementing an open-source FL framework named FastFederatedLearning (FFL). FFL is implemented in C/C++, focusing on code performance, and allows the user to specify any communication graph between clients and servers involved in the federation, ensuring customizability. FFL is tested against Intel OpenFL, achieving consistent speedups over different computational platforms (x86-64, ARM-v8, RISC-V), ranging from 2.5x and 3.69x. We aim to wrap FFL with a Python interface to ease its use and implement a middleware for different communication backends to be used. We aim to build dynamic federations in which relations between clients and servers are not static, giving life to an environment where federations can be seen as long-time evolving structures and exploited as services.

@misc{23:europar:phdtalk,
address = {Limassol, Cyprus},
author = {Gianluca Mittone and Robert Birke and Marco Aldinucci},
abstract = {Federated Learning (FL) is experiencing a substantial research interest, with many frameworks being developed to allow practitioners to build federations easily and quickly.
Most of these efforts do not consider two main aspects that are key to Machine Learning (ML) software: customizability and performance.
This research addresses these issues by implementing an open-source FL framework named FastFederatedLearning (FFL).
FFL is implemented in C/C++, focusing on code performance, and allows the user to specify any communication graph between clients and servers involved in the federation, ensuring customizability.
FFL is tested against Intel OpenFL, achieving consistent speedups over different computational platforms (x86-64, ARM-v8, RISC-V), ranging from 2.5x and 3.69x.
We aim to wrap FFL with a Python interface to ease its use and implement a middleware for different communication backends to be used.
We aim to build dynamic federations in which relations between clients and servers are not static, giving life to an environment where federations can be seen as long-time evolving structures and exploited as services.},
keywords = {invited, eupilot, icsc},
month = {August},
howpublished = {29th International European Conference on Parallel and Distributed Computing (Euro-Par '23)},
title = {Efficiently Distributed Federated Learning},
note = {},
year = {2023},
url = {https://datacloud.di.unito.it/index.php/s/pT3qxkwzzsHR3nS}
}

M. Aldinucci and R. Damiano, OpenScience@UNITO: Milestone 1: a novel continuous training course for data stewards, University of Torino research commission Torino, Italy: , OpenScience group of the University of Torino, July, 2023.
[BibTeX] [Download PDF]

@misc{23:openscience:cuap,
address = {Torino, Italy},
author = {Marco Aldinucci and Rossana Damiano},
keywords = {openscience},
month = {July},
howpublished = {University of Torino research commission},
title = {OpenScience@UNITO: Milestone 1:
a novel continuous training course for data stewards},
note = {OpenScience group of the University of Torino},
year = {2023},
url ={https://datacloud.di.unito.it/index.php/s/r7QgMWTRGF8b2yq}
}

In recent years we have understood the importance of analyzing and sequencing human genetic variation. A relevant aspect that emerged from the Covid-19 pandemic was the need to obtain results very quickly; this involved using High-Performance Computing (HPC) environments to execute the Next Generation Sequencing (NGS) pipeline. However, HPC is not always the most suitable environment for the entire execution of a pipeline, especially when it involves many heterogeneous tools. The ability to execute parts of the pipeline on different environments can lead to higher performance but also cheaper executions. This work shows the design and optimization process that led us to a state-of-the-art Variant Calling hybrid workflow based on the StreamFlow Workflow Management System (WfMS). We also compare StreamFlow with Snakemake, an established WfMS targeting HPC facilities, observing comparable performance on single environments and satisfactory improvements with a hybrid cloud-HPC configuration.

@misc{23:mulone:wide:vcp,
abstract = {In recent years we have understood the importance of analyzing and sequencing human genetic variation. A relevant aspect that emerged from the Covid-19 pandemic was the need to obtain results very quickly; this involved using High-Performance Computing (HPC) environments to execute the Next Generation Sequencing (NGS) pipeline. However, HPC is not always the most suitable environment for the entire execution of a pipeline, especially when it involves many heterogeneous tools. The ability to execute parts of the pipeline on different environments can lead to higher performance but also cheaper executions. This work shows the design and optimization process that led us to a state-of-the-art Variant Calling hybrid workflow based on the StreamFlow Workflow Management System (WfMS). We also compare StreamFlow with Snakemake, an established WfMS targeting HPC facilities, observing comparable performance on single environments and satisfactory improvements with a hybrid cloud-HPC configuration.},
address = {Torino, Italy},
author = {Alberto Mulone and Sherine Awad and Davide Chiarugi and Marco Aldinucci},
howpublished = {47th {IEEE} Annual Computers, Software, and Applications Conference, {COMPSAC} 2023},
keywords = {across, icsc, streamflow},
title = {Porting the {V}ariant {C}alling {P}ipeline for {NGS} data in cloud-{HPC} environment},
year = {2023},
month = {Jun},
url = {https://datacloud.di.unito.it/index.php/s/zNLj3LCZNsNxHwy}
}

Decentralised Machine Learning (DML) enables collaborative machine learning without centralised input data. Federated Learning (FL) and Edge Inference are examples of DML. While tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel processors (e.g., RISC-V), non-fully connected network topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing us to map DML schemes to an underlying middleware, i.e. the FastFlow parallel programming library. We experiment with it by generating different working DML schemes on x86-64 and ARM platforms and an emerging RISC-V one. We characterise the performance and energy efficiency of the presented schemes and systems. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge.

@misc{23:ACMCF,
abstract = {Decentralised Machine Learning (DML) enables collaborative machine learning without centralised input data. Federated Learning (FL) and Edge Inference are examples of DML. While tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel processors (e.g., RISC-V), non-fully connected network topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing us to map DML schemes to an underlying middleware, i.e. the FastFlow parallel programming library. We experiment with it by generating different working DML schemes on x86-64 and ARM platforms and an emerging RISC-V one. We characterise the performance and energy efficiency of the presented schemes and systems. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge.},
author = {Gianluca Mittone and Nicolò Tonci and Robert Birke and Iacopo Colonnelli and Doriana Medić and Andrea Bartolini and Roberto Esposito and Emanuele Parisi and Francesco Beneventi and Mirko Polato and Massimo Torquati and Luca Benini and Marco Aldinucci},
howpublished = {20th ACM international conference on computing frontiers (CF '23)},
keywords = {invited, eupilot, icsc},
month = {May},
note = {Invited talk},
title = {Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning},
url = {https://datacloud.di.unito.it/index.php/s/BYyqZbHzzN4DL8Z},
year = {2023}
}

Aggregating pharmaceutical data in the drug-target interaction (DTI) domain can potentially deliver life-saving breakthroughs. It is, however, notoriously difficult due to regulatory constraints and commercial interests. This work proposes the application of federated learning, which is reconcilable with the industry’s constraints. It does not require sharing any information that would reveal the entities’ data or any other high-level summary. When used on a representative GraphDTA model and the KIBA dataset, it achieves up to 15\% improved performance relative to the best available non-privacy preserving alternative. Our extensive battery of experiments shows that, unlike in other domains, the non-IID data distribution in the DTI datasets does not deteriorate FL performance. Additionally, we identify a material trade-off between the benefits of adding new data and the cost of adding more clients.

@misc{23:WWW,
abstract = {Aggregating pharmaceutical data in the drug-target interaction (DTI) domain can potentially deliver life-saving breakthroughs. It is, however, notoriously difficult due to regulatory constraints and commercial interests. This work proposes the application of federated learning, which is reconcilable with the industry's constraints. It does not require sharing any information that would reveal the entities' data or any other high-level summary. When used on a representative GraphDTA model and the KIBA dataset, it achieves up to 15\% improved performance relative to the best available non-privacy preserving alternative. Our extensive battery of experiments shows that, unlike in other domains, the non-IID data distribution in the DTI datasets does not deteriorate FL performance. Additionally, we identify a material trade-off between the benefits of adding new data and the cost of adding more clients.},
author = {Gianluca Mittone and Filip Svoboda and Marco Aldinucci and Nicholas D. Lane and Pietro Lio'},
howpublished = {2023 ACM international Web Conference (WWW '23)},
keywords = {invited, eupilot, icsc},
month = {May},
note = {Invited talk},
title = {A Federated Learning Benchmark for Drug-Target Interaction},
url = {https://datacloud.di.unito.it/index.php/s/js7go3EorZxSLn9},
year = {2023}
}

I. Colonnelli, UNITO tools presentation, CN HPC Flagship 3 Working Day Bologna, Italy: , May, 2023.
[BibTeX] [Download PDF]

@misc{23:FL3WorkingDay,
address = {Bologna, Italy},
author = {Iacopo Colonnelli},
keywords = {streamflow, jupyter-workflow},
month = {May},
howpublished = {CN HPC Flagship 3 Working Day},
title = {{UNITO} tools presentation},
url = {https://datacloud.di.unito.it/index.php/s/fgHbnLDQSFtcwLd},
year = {2023}
}

I. Colonnelli, Workflows and the Common Workflow Language (CWL), OSA2Micro: An Open Science Approach to Microbiology data integration Torino, Italy: , Invited talk, May, 2023.
[BibTeX] [Download PDF]

@misc{23:SA2Micro,
address = {Torino, Italy},
author = {Iacopo Colonnelli},
keywords = {streamflow},
month = {May},
howpublished = {OSA2Micro: An Open Science Approach to Microbiology data integration},
title = {Workflows and the Common Workflow Language ({CWL})},
url = {https://datacloud.di.unito.it/index.php/s/NHWWzMMaQgAsA52},
note = {Invited talk},
year = {2023}
}

I. Colonnelli, CWL for HPC: are we there yet?, 2023 CWL Conference Heidelberg, Germany: , Invited talk, March, 2023.
[BibTeX] [Abstract] [Download PDF]

Modern HPC applications are becoming so heterogeneous and complex that a modular approach to their design, deployment and orchestration is now necessary. This talk explores the benefits of using a vendor-agnostic workflow language (CWL) coupled with a hybrid workflow management system (StreamFlow) in the HPC ecosystem. Also, it will examine the requirements needed to model HPC applications effectively, the CWL’s readiness to meet such requirements, and the proposals made to improve the language where needed. Four real use cases will drive the discussion: the ACROSS Project (G.A. n. 955648), where CWL is the primary interface to model three HPC workflows, and the EUPEX Project (G.A. n. 101033975), where StreamFlow will be used for the rapid prototyping of a seismic engineering HPC application for a Modular Supercomputing Architecture (MSA) system.

@misc{23:CWLConference,
abstract = {Modern HPC applications are becoming so heterogeneous and complex that a modular approach to their design, deployment and orchestration is now necessary. This talk explores the benefits of using a vendor-agnostic workflow language (CWL) coupled with a hybrid workflow management system (StreamFlow) in the HPC ecosystem. Also, it will examine the requirements needed to model HPC applications effectively, the CWL’s readiness to meet such requirements, and the proposals made to improve the language where needed. Four real use cases will drive the discussion: the ACROSS Project (G.A. n. 955648), where CWL is the primary interface to model three HPC workflows, and the EUPEX Project (G.A. n. 101033975), where StreamFlow will be used for the rapid prototyping of a seismic engineering HPC application for a Modular Supercomputing Architecture (MSA) system.},
address = {Heidelberg, Germany},
author = {Iacopo Colonnelli},
howpublished = {2023 CWL Conference},
keywords = {invited, streamflow, across, eupex},
month = {March},
note = {Invited talk},
title = {{CWL} for {HPC}: are we there yet?},
url = {https://datacloud.di.unito.it/index.php/s/CMCd5LiZeXsxwEg},
year = {2023}
}

A hands-on training offer for Standardised Workflows in EBRAINS. A short presentation will be used as an introduction, while the main hands-on session will provide information about Writing and Executing Standardised Workflows. TC will give some guidelines, so attendees can experiment with writing CWL tools and workflows and then they will be given access to VM to execute these workflows. The Workflows Dashboard will be also presented during the same session, offering to the attendees the opportunity to understand the different functionalities, use it with TC support and provide useful comments.

@misc{23:HBPSummit,
abstract = {A hands-on training offer for Standardised Workflows in EBRAINS. A short presentation will be used as an introduction, while the main hands-on session will provide information about Writing and Executing Standardised Workflows. TC will give some guidelines, so attendees can experiment with writing CWL tools and workflows and then they will be given access to VM to execute these workflows. The Workflows Dashboard will be also presented during the same session, offering to the attendees the opportunity to understand the different functionalities, use it with TC support and provide useful comments.},
address = {Marseille, France},
author = {Sofia Karvounari and Eleni Mathioulaki and Michael R. Crusoe and Iacopo Colonnelli},
howpublished = {Human Brain Project Summit 2023},
keywords = {invited, streamflow, across, eupex, space},
month = {March},
note = {Invited talk},
title = {Standardised Workflows at {EBRAINS}},
url = {https://datacloud.di.unito.it/index.php/s/K5YQKTsX9N7NLT8},
year = {2023}
}

M. Aldinucci, Experimenting with Systems for Decentralized Machine Learning, NVidia GTC 2023 , March, 2023.
[BibTeX] [Abstract] [Download PDF]

Decentralized machine learning (DML) enables collaborative machine learning without centralized input data. Federated learning (FL) and edge inference (EI) are examples of DML. Collaboration naturally happens at the edge of a distributed system with inherently distributed data. While tools for DML are starting to flourish, much needs to be done to get more flexible and portable tools to experiment with novel techniques, non-fully connected topologies, multiple data domains, and asynchronous collaboration schemes. We’ll present recent advances in DML, aiming to improve usability in data centers and, at the edge, to widen the class of models extending FL to non-DNN paradigms, to improve the accuracy of models controlling normalization and frequency of communications, and to boost data privacy though generative adversarial networks. Prerequisites: Intermediate understanding of machine learning methods and distributed & parallel computing.

@Misc{23:gtc:fl,
OPTkey = {},
author = {Marco Aldinucci},
title = {Experimenting with Systems for Decentralized Machine Learning},
howpublished = {NVidia GTC 2023},
month = {March},
year = {2023},
OPTnote = {},
OPTannote = {},
keywords = {hpc4ai, eupex, across, textarossa, admire, eupilot, epi, space, eumaster4hpc},
abstract = {Decentralized machine learning (DML) enables collaborative machine learning without centralized input data. Federated learning (FL) and edge inference (EI) are examples of DML. Collaboration naturally happens at the edge of a distributed system with inherently distributed data. While tools for DML are starting to flourish, much needs to be done to get more flexible and portable tools to experiment with novel techniques, non-fully connected topologies, multiple data domains, and asynchronous collaboration schemes. We'll present recent advances in DML, aiming to improve usability in data centers and, at the edge, to widen the class of models extending FL to non-DNN paradigms, to improve the accuracy of models controlling normalization and frequency of communications, and to boost data privacy though generative adversarial networks. Prerequisites: Intermediate understanding of machine learning methods and distributed & parallel computing.},
url = {https://datacloud.di.unito.it/index.php/s/oyLt7xwkbKxz65c}
}

M. Aldinucci, HPC4AI: The Research on AI beyond the public cloud, CENTAI kick-off meeting Torino, Italy: , March, 2023.
[BibTeX] [Download PDF]

@Misc{23:CENTAI:hpc4ai,
OPTkey = {},
author = {Marco Aldinucci},
title = {{HPC4AI}: The Research on {AI} beyond the public cloud},
howpublished = {CENTAI kick-off meeting},
month = {March},
year = {2023},
address = {Torino, Italy},
OPTnote = {},
OPTannote = {},
keywords = {invited, hpc4ai, eupex, across, textarossa, admire, eupilot, epi, eumaster4hpc, space, brainteaser},
abstract = {},
url = {https://datacloud.di.unito.it/index.php/s/PZXjPm8sfKTmTGb}
}

M. Aldinucci, From HPC4AI to ICSC living lab: Where systems are the research, Dell Advanced Computing Workshop 2023: HPC and Beyond Bologna, Italy: , Feb, 2023.
[BibTeX] [Download PDF]

@Misc{23:Dell:hpc4ai,
OPTkey = {},
author = {Marco Aldinucci},
title = {From {HPC4AI} to {ICSC} living lab: Where systems are the research},
howpublished = {Dell Advanced Computing Workshop 2023: HPC and Beyond},
month = {Feb},
year = {2023},
address = {Bologna, Italy},
OPTnote = {},
OPTannote = {},
keywords = {invited, hpc4ai, futurehpc, eupex, textarossa, admire, eupilot},
abstract = {},
url = {https://datacloud.di.unito.it/index.php/s/M5QRJyDxyxokcfL}
}

G. Mittone, Paving the way to innovative tools for Federated Learning, 2023 HiPEAC Conference Toulouse, France: , Invited talk, February, 2023.
[BibTeX] [Abstract] [Download PDF]

Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs). On the one hand, this allowed its development and widespread use as DNNs proliferated. On the other hand, it neglected all those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only allow training DNNs reinforces this problem. To address the lack of FL solutions for non-DNN-based use cases, we propose MAFL (Model-Agnostic Federated Learning). MAFL marries a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel® OpenFL. MAFL is the first FL system not tied to any specific type of machine learning model, allowing exploration of FL scenarios beyond DNNs and trees. Furthermore, tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel systems (e.g., RISC-V), non-fully connected topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing to map DML schemes to an underlying middleware, i.e. the \ff parallel programming library. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge.

@misc{23:hipeac,
abstract = {Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs). On the one hand, this allowed its development and widespread use as DNNs proliferated. On the other hand, it neglected all those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only allow training DNNs reinforces this problem. To address the lack of FL solutions for non-DNN-based use cases, we propose MAFL (Model-Agnostic Federated Learning). MAFL marries a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel® OpenFL. MAFL is the first FL system not tied to any specific type of machine learning model, allowing exploration of FL scenarios beyond DNNs and trees. Furthermore, tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel systems (e.g., RISC-V), non-fully connected topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing to map DML schemes to an underlying middleware, i.e. the \ff parallel programming library. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge.},
address = {Toulouse, France},
author = {Gianluca Mittone},
howpublished = {2023 HiPEAC Conference},
keywords = {invited, eupilot},
month = {February},
note = {Invited talk},
title = {Paving the way to innovative tools for Federated Learning},
url = {https://datacloud.di.unito.it/index.php/s/2GtxPidHq79RTzA},
year = {2023}
}

D. Medić and M. Aldinucci, Towards formal model for location aware workflows Torino, Italy: , 2023.
[BibTeX] [Abstract] [Download PDF]

Designing complex applications and executing them on large-scale topologies of heterogeneous architectures is becoming increasingly crucial in many scientific domains. As a result, diverse workflow modelling paradigms are developed, most of them with no formalisation provided. In these circumstances, comparing two different models or switching from one system to the other becomes a hard nut to crack. This paper investigates the capability of process algebra to model a location aware workflow system. Distributed $\pi$-calculus is considered as the base of the formal model due to its ability to describe the communicating components that change their structure as an outcome of the communication. Later, it is discussed how the base model could be extended or modified to capture different features of location aware workflow system. The intention of this paper is to highlight the fact that due to its flexibility, $\pi$-calculus, could be a good candidate to represent the behavioural perspective of the workflow system.

@misc{23:wide:medic,
address = {Torino, Italy},
author = {Doriana Medi\'{c} and Marco Aldinucci},
abstract = {Designing complex applications and executing them on large-scale topologies of heterogeneous architectures is becoming increasingly crucial in many scientific domains. As a result, diverse workflow modelling paradigms are developed, most of them with no formalisation provided. In these circumstances, comparing two different models or switching from one system to the other becomes a hard nut to crack.
This paper investigates the capability of process algebra to model a location aware workflow system. Distributed $\pi$-calculus is considered as the base of the formal model due to its ability to describe the communicating components that change their structure as an outcome of the communication. Later, it is discussed how the base model could be extended or modified to capture different features of location aware workflow system.
The intention of this paper is to highlight the fact that due to its flexibility, $\pi$-calculus, could be a good candidate to represent the behavioural perspective of the workflow system.},
keywords = {eupex, icsc},
title = {Towards formal model for location aware workflows},
year = {2023},
url = {https://datacloud.di.unito.it/index.php/s/SgTbE2Ltbg38KtS}
}

B. Casella, G. Mittone, Y. Arfat, M. Aldinucci, and R. Esposito, Towards Machine Learning on the edge: the opportunities of Federated Learning, 3rd Italian Workshop on HPC Turin, Italy: , Invited talk, Sep, 2022.
[BibTeX] [Abstract] [Download PDF]

Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs). On the one hand, this allowed its development and widespread use as DNNs proliferated. On the other hand, it neglected all those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only allow training DNNs reinforces this problem. To address the lack of FL solutions for non-DNN-based use cases, we propose MAFL (Model-Agnostic Federated Learning). MAFL marries a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel® OpenFL. MAFL is the first FL system not tied to any specific type of machine learning model, allowing exploration of FL scenarios beyond DNNs and trees. Furthermore, tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel systems (e.g., RISC-V), non-fully connected topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing to map DML schemes to an underlying middleware, i.e. the \ff parallel programming library.

@misc{22:ITWSHPC,
abstract = {Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs). On the one hand, this allowed its development and widespread use as DNNs proliferated. On the other hand, it neglected all those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only allow training DNNs reinforces this problem. To address the lack of FL solutions for non-DNN-based use cases, we propose MAFL (Model-Agnostic Federated Learning). MAFL marries a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel® OpenFL. MAFL is the first FL system not tied to any specific type of machine learning model, allowing exploration of FL scenarios beyond DNNs and trees. Furthermore, tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel systems (e.g., RISC-V), non-fully connected topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing to map DML schemes to an underlying middleware, i.e. the \ff parallel programming library.},
address = {Turin, Italy},
author = {Bruno Casella and Gianluca Mittone and Yasir Arfat and Marco Aldinucci and Roberto Esposito},
howpublished = {3rd Italian Workshop on HPC},
keywords = {invited},
month = {Sep},
note = {Invited talk},
title = {Towards Machine Learning on the edge: the opportunities of Federated Learning},
url = {https://datacloud.di.unito.it/index.php/s/X3bDLCBeZMo4qr5},
year = {2022}
}

I. Colonnelli and M. Aldinucci, CINI HPC-KTT: HPC Key Technologies and Tools National Lab, NVIDIA HPC Roundtable Casalecchio di Reno, Italy: , Invited talk, Sep, 2022.
[BibTeX] [Download PDF]

@Misc{22:nvidia_hpc_roundtable,
author = {Iacopo Colonnelli and Marco Aldinucci},
title = {{CINI HPC-KTT}: {HPC} {K}ey {T}echnologies and {T}ools National Lab},
howpublished = {NVIDIA HPC Roundtable},
month = {Sep},
year = {2022},
note = {Invited talk},
address = {Casalecchio di Reno, Italy},
keywords = {invited, eupex, across, admire, eupilot, textarossa, eumaster4hpc},
abstract = {},
annote = {},
url ={https://datacloud.di.unito.it/index.php/s/9EQniZ2dGzdJ26f},
}

B. Casella, Benchmarking FedAvg and FedCurv for Image Classification Tasks, ITADATA Milan, Italy: , Sep, 2022.
[BibTeX] [Abstract] [Download PDF]

Presentation of the paper “Benchmarking FedAvg and FedCurv for Image Classification Tasks” to the first italian conference on Big Data and Data Science

@misc{22:itadata,
abstract = {Presentation of the paper "Benchmarking FedAvg and FedCurv for Image Classification Tasks" to the first italian conference on Big Data and Data Science},
author = {Bruno Casella},
title = {Benchmarking FedAvg and FedCurv for Image Classification Tasks
},
howpublished = {ITADATA},
month = {Sep},
year = {2022},
address = {Milan, Italy},
keywords = {eupilot},
annote = {},
url ={https://datacloud.di.unito.it/index.php/s/6XaEXnAowRrAHGL},
}

M. Aldinucci, Il calcolo parallelo: una storia di metodi e algoritmi raccontata dalle macchine, Olimpiadi di Informatica Biella, Italy: , Invited talk, Sep, 2022.
[BibTeX] [Download PDF]

@misc{22:olimpiadi:cs,
abstract = {Lectio Magistralis alle finali nazionali delle Olimpiadi di Informatica 2022},
author = {Marco Aldinucci},
title = {Il calcolo parallelo: una storia di metodi e algoritmi raccontata dalle macchine
},
howpublished = {Olimpiadi di Informatica},
month = {Sep},
year = {2022},
note = {Invited talk},
address = {Biella, Italy},
keywords = {invited, eupex, across, textarossa, admire, eupilot, eumaster4hpc},
abstract = {},
annote = {},
url ={https://datacloud.di.unito.it/index.php/s/7ZdfLkn3NetzXCN},
}

@misc{22:soiel:ai,
abstract = {Innanzitutto, le infrastrutture HPC stanno adottando le GPU per il loro rapporto prestazioni per watt superiore rispetto ai multicore generici. In secondo luogo, i flussi di lavoro scientifici di prossima generazione stanno integrando passaggi basati sull'intelligenza artificiale per la loro precisione nell'approssimazione e nell'analisi di fenomeni complessi. In terzo luogo, l'IA e in particolare il Machine Learning (ML) rappresentano un carico di lavoro perfetto per le GPU in termini di prestazioni e tempo di sviluppo. Oggi non possiamo ancora chiudere il cerchio eseguendo senza problemi carichi di lavoro scientifici abilitati all'intelligenza artificiale nelle infrastrutture HPC perché il loro software di sistema e gli strumenti di sviluppo non sono progettati per i carichi di lavoro moderni, come i framework ML progettati per il cloud. È probabile che la convergenza HPC-cloud colmi il divario.
Nel talk verranno presentate le infrastrutture e gli strumenti sviluppati all'Università di Torino per la convergenza HPC-cloud (es. HPC4AI, StreamFlow, CAPIO, Jupyter-workflow) e come sono stati utilizzati per le applicazioni di intelligenza artificiale, come la diagnosi spiegabile di polmonite COVID-19 e la tutela della privacy AI. L'esperienza maturata nella progettazione e gestione di HPC4AI costituisce il cuore della progettazione del laboratorio di contaminazione del "FutureHPC" di Torino secondo il Centro Nazionale "HPC, BigData e Quantum Computing" finanziato dal PNRR con 320M€ che dovrebbe essere operativo dal 1 settembre 2022. L'obiettivo finale del laboratorio di contaminazione è sviluppare relazioni e collaborazioni tra industria e università.},
author = {Marco Aldinucci},
title = {La convergenza HPC-cloud è l'anello mancante tra il calcolo scientifico e l'IA applicata},
howpublished = {Intelligenza Artificiale e Business Applications},
month = {Sep},
year = {2022},
note = {Invited talk},
address = {Virtual event},
keywords = {invited, eupex, across, textarossa, admire, eupilot, eumaster4hpc},
abstract = {},
annote = {},
url ={https://datacloud.di.unito.it/index.php/s/xCQSqJ8bCKCXMK9},
}

I. Colonnelli and M. Aldinucci, Hybrid Workflows For Large-Scale Scientific Applications, 6th EAGE High Performance Computing Workshop Milano, Italy: , Sep, 2022.
[BibTeX] [Download PDF]

@Misc{22:eage,
abstract = {Large-scale scientific applications are facing an irreversible transition from monolithic, high-performance oriented codes to modular and polyglot deployments of specialised (micro-)services. The reasons behind this transition are many: coupling of standard solvers with Deep Learning techniques, offloading of data analysis and visualisation to Cloud, and the advent of specialised hardware accelerators. Topology-aware Workflow Management Systems (WMSs) play a crucial role. In particular, topology-awareness allows an explicit mapping of workflow steps onto heterogeneous locations, allowing automated executions on top of hybrid architectures (e.g., cloud+HPC or classical+quantum). Plus, topology-aware WMSs can offer non-functional requirements OOTB, e.g. components’ life-cycle orchestration, secure and efficient data transfers, fault tolerance, and cross-cluster execution of urgent workloads. Augmenting interactive Jupyter Notebooks with distributed workflow capabilities allows domain experts to prototype and scale applications using the same technological stack, while relying on a feature-rich and user-friendly web interface. This abstract will showcase how these general methodologies can be applied to a typical geoscience simulation pipeline based on the Full Wavefront Inversion (FWI) technique. In particular, a prototypical Jupyter Notebook will be executed interactively on Cloud. Preliminary data analyses and post-processing will be executed locally, while the computationally demanding optimisation loop will be scheduled on a remote HPC cluster.},
author = {Iacopo Colonnelli and Marco Aldinucci},
title = {Hybrid Workflows For Large-Scale Scientific Applications},
howpublished = {6th EAGE High Performance Computing Workshop},
month = {Sep},
year = {2022},
note = {},
address = {Milano, Italy},
keywords = {eupex, across, textarossa, jupyter-workflow},
abstract = {},
annote = {},
url ={https://datacloud.di.unito.it/index.php/s/GScPS5LCPdt6Yoo},
}

@Misc{22:itwshpc,
author = {Iacopo Colonnelli and Barbara Cantalupo and Doriana Medi\'{c} and Marco Aldinucci},
title = {Hybrid workflows for heterogeneous distributed computing},
howpublished = {3rd Italian Workshop on HPC (ITWSHPC)},
month = {Sep},
year = {2022},
note = {},
address = {Torino, Italy},
keywords = {eupex, across, admire, eupilot, textarossa, eumaster4hpc},
abstract = {},
annote = {},
url ={https://datacloud.di.unito.it/index.php/s/ienbcA2DJ26aioE},
}

I. Colonnelli and D. Tranchitella, Dossier: multi-tenant distributed Jupyter Notebooks, DoK Talks 141 Virtual event: , Invited talk, July, 2022.
[BibTeX] [Abstract] [Download PDF]

When providing data analysis as a service, one must tackle several problems. Data privacy and protection by design are crucial when working on sensitive data. Performance and scalability are fundamental for compute-intensive workloads, e.g. training Deep Neural Networks. User-friendly interfaces and fast prototyping tools are essential to allow domain experts to experiment with new techniques. Portability and reproducibility are necessary to assess the actual value of results. Kubernetes is the best platform to provide reliable, elastic, and maintainable services. However, Kubernetes alone is not enough to achieve large-scale multi-tenant reproducible data analysis. OOTB support for multi-tenancy is too rough, with only two levels of segregation (i.e. the single namespace or the entire cluster). Offloading computation to off-cluster resources is non-trivial and requires the user’s manual configuration. Also, Jupyter Notebooks per se cannot provide much scalability (they execute locally and sequentially) and reproducibility (users can run cells in any order and any number of times). The Dossier platform allows system administrators to manage multi-tenant distributed Jupyter Notebooks at the cluster level in the Kubernetes way, i.e. through CRDs. Namespaces are aggregated in Tenants, and all security and accountability aspects are managed at that level. Each Notebook spawns into a user-dedicated namespace, subject to all Tenant-level constraints. Users can rely on provisioned resources, either in-cluster worker nodes or external resources like HPC facilities. Plus, they can plug their computing nodes in a BYOD fashion. Notebooks are interpreted as distributed workflows, where each cell is a task that one can offload to a different location in charge of its execution.

@Misc{22:data-on-kubernetes,
OPTkey = {},
author = {Iacopo Colonnelli and Dario Tranchitella},
abstract = {When providing data analysis as a service, one must tackle several problems. Data privacy and protection by design are crucial when working on sensitive data. Performance and scalability are fundamental for compute-intensive workloads, e.g. training Deep Neural Networks. User-friendly interfaces and fast prototyping tools are essential to allow domain experts to experiment with new techniques. Portability and reproducibility are necessary to assess the actual value of results. Kubernetes is the best platform to provide reliable, elastic, and maintainable services. However, Kubernetes alone is not enough to achieve large-scale multi-tenant reproducible data analysis. OOTB support for multi-tenancy is too rough, with only two levels of segregation (i.e. the single namespace or the entire cluster). Offloading computation to off-cluster resources is non-trivial and requires the user's manual configuration. Also, Jupyter Notebooks per se cannot provide much scalability (they execute locally and sequentially) and reproducibility (users can run cells in any order and any number of times). The Dossier platform allows system administrators to manage multi-tenant distributed Jupyter Notebooks at the cluster level in the Kubernetes way, i.e. through CRDs. Namespaces are aggregated in Tenants, and all security and accountability aspects are managed at that level. Each Notebook spawns into a user-dedicated namespace, subject to all Tenant-level constraints. Users can rely on provisioned resources, either in-cluster worker nodes or external resources like HPC facilities. Plus, they can plug their computing nodes in a BYOD fashion. Notebooks are interpreted as distributed workflows, where each cell is a task that one can offload to a different location in charge of its execution.},
title = {Dossier: multi-tenant distributed {J}upyter {N}otebooks},
howpublished = {DoK Talks 141},
month = {July},
year = {2022},
keywords = {jupyter-workflow, across, deephealth, hpc4ai},
note = {Invited talk},
address = {Virtual event},
OPTannote = {},
url = {https://datacloud.di.unito.it/index.php/s/RNqTGmTqWS66qHT}
}

@Misc{22:healthycloud-workshop,
OPTkey = {},
author = {Iacopo Colonnelli},
title = {{StreamFlow}},
howpublished = {2nd HealthyCloud Workshop: Analysis of existing orchestration mechanisms for distributed computational analyses},
month = {July},
year = {2022},
keywords = {invited, streamflow, deephealth, across, eupex, textarossa},
note = {Invited talk},
address = {Virtual event},
OPTannote = {},
url = {https://datacloud.di.unito.it/index.php/s/Taz8qtzmkmn9ffT}
}

M. Aldinucci, From small files to no files, 6th Workshop on Performance and Scalability of Storage Systems Paris, France: , Invited talk, June, 2022.
[BibTeX] [Abstract] [Download PDF]

Modern distributed high-performance storage systems saturate the network bandwidth, and the margins for improvement at the software level are tiny. Due to metadata access, they might be troubled with massive access to small files. An example is the Software Heritage (SH) dataset, half petabytes of files with an average size of 3kBytes (Terabytes of metadata). While working with SH, we developed the idea of substituting files with in-memory streams. We did it living in dread with the fear of asking application programmers to rewrite their lovely antique legacy code exploiting the POSIX interface, and up to now, we did not. In the talk, we will introduce CAPIO (Cross-Application Programmable I/O) design principles and the current state of development of the prototype.

@Misc{22:p3s:capio,
author = {Marco Aldinucci},
title = {From small files to no files},
howpublished = {6th Workshop on Performance and Scalability of Storage Systems},
month = {June},
year = {2022},
note = {Invited talk},
address = {Paris, France},
keywords = {invited, admire, eupex},
abstract = {Modern distributed high-performance storage systems saturate the network bandwidth, and the margins for improvement at the software level are tiny. Due to metadata access, they might be troubled with massive access to small files. An example is the Software Heritage (SH) dataset, half petabytes of files with an average size of 3kBytes (Terabytes of metadata). While working with SH, we developed the idea of substituting files with in-memory streams. We did it living in dread with the fear of asking application programmers to rewrite their lovely antique legacy code exploiting the POSIX interface, and up to now, we did not. In the talk, we will introduce CAPIO (Cross-Application Programmable I/O) design principles and the current state of development of the prototype.},
annote = {https://per3s.github.io},
url ={https://datacloud.di.unito.it/index.php/s/KLDi87xQmX86iXg}
}

M. Aldinucci, The Italian HPC ecosystem and the next generation of EuroHPC CoE, EuroHPC EoCoE final summit Napoli, Italy: , Invited talk, June, 2022.
[BibTeX] [Download PDF]

@Misc{22:eocoe:summit,
abstract = {The talk presents the main investments currently ongoing in Italy in the HPC area as well as the activity of Italian stakeholders within EuroHPC. The novel Italian National Centre on HPC (ICSC) is introduced.},
author = {Marco Aldinucci},
title = {The Italian HPC ecosystem and the next generation of EuroHPC CoE},
howpublished = {EuroHPC EoCoE final summit},
month = {June},
year = {2022},
note = {Invited talk},
address = {Napoli, Italy},
keywords = {invited, admire, eupex, across, eupilot, textarossa, eumaster4hpc, icsc},
abstract = {},
annote = {},
url ={https://datacloud.di.unito.it/index.php/s/AH5Ms3NekeoEooB}
}

M. Aldinucci, EuroHPC and the Italian HPC ecosystem, Critical Infrastructure Protection Forum – EuroCC Romania Bucharest, Romania: , Invited talk, June, 2022.
[BibTeX] [Download PDF]

@Misc{22:cip:romania,
abstract = {The talk presents the main investments currently ongoing in Italy in the HPC area as well as the activity of Italian stakeholders within EuroHPC. The novel Italian National Centre on HPC (ICSC) is introduced.},
author = {Marco Aldinucci},
title = {EuroHPC and the Italian HPC ecosystem},
howpublished = {Critical Infrastructure Protection Forum - EuroCC Romania},
month = {June},
year = {2022},
note = {Invited talk},
address = {Bucharest, Romania},
keywords = {invited, admire, eupex, across, eupilot, textarossa, eumaster4hpc, icsc},
abstract = {},
annote = {},
url ={https://datacloud.di.unito.it/index.php/s/5dFFoNsZzwTzQkn}
}

I. Colonnelli and M. Aldinucci, T4.1: Streaming models, TEXTAROSSA General Meeting Roma, Italy: , June, 2022.
[BibTeX] [Download PDF]

@Misc{22:textarossa-ga-meeting,
OPTkey = {},
author = {Iacopo Colonnelli and Marco Aldinucci},
title = {T4.1: Streaming models},
howpublished = {TEXTAROSSA General Meeting},
month = {June},
year = {2022},
keywords = {textarossa},
address = {Roma, Italy},
OPTannote = {},
url = {https://datacloud.di.unito.it/index.php/s/cNBnwSnTc8GiCkN}
}

I. Colonnelli, StreamFlow: a topology-aware WMS, ELIXIR Cloud, Data & AAI Bi-weekly Technical Calls Virtual event: , Invited talk, June, 2022.
[BibTeX] [Download PDF]

@Misc{22:elixir-streamflow,
OPTkey = {},
author = {Iacopo Colonnelli},
title = {{StreamFlow}: a topology-aware {WMS}},
howpublished = {ELIXIR Cloud, Data & AAI Bi-weekly Technical Calls},
month = {June},
year = {2022},
keywords = {invited, streamflow, dephealth, across, eupex, textarossa},
note = {Invited talk},
address = {Virtual event},
OPTannote = {},
url = {https://datacloud.di.unito.it/index.php/s/Z9GsKnRCxmBdMd3}
}

First, HPC infrastructures are embracing GPUs for their superior performance-per-watt ratio against general-purpose multicores. Second, the next-generation scientific workflows are integrating AI-based steps for their accuracy in approximating and analyzing complex phenomena. Third, AI and specifically Machine Learning (ML), is a perfect workload for GPUs in terms of performance and development time. Today, we cannot still close the circle seamlessly running AI-enabled scientific workloads into HPC infrastructures because their system software and development tools are not designed for modern workloads, such as ML frameworks designed for the cloud. HPC-cloud convergence is likely to bridge the gap. In the talk, we will present Streamflow and CAPIO, two development tools for HPC-cloud convergence.

@misc{22:ml4astro,
address = {Catania, Italy},
author = {Marco Aldinucci},
howpublished = {Machine Learning for Astrophysics {(ML4ASTRO)}},
keywords = {keynote, deephealth, eupex, across, eupilot},
month = {June},
title = {{HPC}-cloud convergence is the missing link between scientific computing and applied{-AI}},
note ={Keynote talk},
year = {2022},
url = {https://datacloud.di.unito.it/index.php/s/2SGswkcip7MoMoH},
abstract = {First, HPC infrastructures are embracing GPUs for their superior performance-per-watt ratio against general-purpose multicores. Second, the next-generation scientific workflows are integrating AI-based steps for their accuracy in approximating and analyzing complex phenomena. Third, AI and specifically Machine Learning (ML), is a perfect workload for GPUs in terms of performance and development time. Today, we cannot still close the circle seamlessly running AI-enabled scientific workloads into HPC infrastructures because their system software and development tools are not designed for modern workloads, such as ML frameworks designed for the cloud. HPC-cloud convergence is likely to bridge the gap. In the talk, we will present Streamflow and CAPIO, two development tools for HPC-cloud convergence.}
}

M. Aldinucci, Cognitive continuum: a game theoretical approach, HiPEAC Vision meeting, Brussels, 16 May 2022 Brussels, Belgium: , May, 2022.
[BibTeX] [Download PDF]

@Misc{22:hipeacvision:fl,
abstract = {Cognitive continuum: a game theoretical approach, (maybe) data operations are too basic: read, write, copy, remove … The talk is aimed to contribute to the forthcoming HiPEAC Vision document},
author = {Marco Aldinucci},
title = {Cognitive continuum: a game theoretical approach},
howpublished = {HiPEAC Vision meeting, Brussels, 16 May 2022},
month = {May},
year = {2022},
note = {},
address = {Brussels, Belgium},
keywords = {invited, admire, eupex, across, eupilot, textarossa, eumaster4hpc, brainteaser},
abstract = {},
annote = {},
url ={https://datacloud.di.unito.it/index.php/s/453HWfmrQyo7j9E}
}

G. Mittone, Python for the human heart: a case study, 2022 Python Conference Florence, Italy: , Invited talk, May, 2022.
[BibTeX] [Abstract] [Download PDF]

Presentation of the “Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets” paper at the PyCon22 DataBeer event.

@misc{22:PyCon,
abstract = {Presentation of the "Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets" paper at the PyCon22 DataBeer event.},
address = {Florence, Italy},
author = {Gianluca Mittone},
howpublished = {2022 Python Conference},
keywords = {invited},
month = {May},
note = {Invited talk},
title = {Python for the human heart: a case study},
url = {https://datacloud.di.unito.it/index.php/s/fERJB5xeE6cXm4N},
year = {2022}
}

M. Aldinucci, Da HPC4AI al living lab dello spoke FutureHPC del Centro Nazionale HPC, Condivisioni, Conferenza GARR 2022 Palermo, Italy: , Keynote talk, May, 2022.
[BibTeX] [Abstract] [Download PDF]

HPC4AI is an open-access laboratory of the University of Turin open to researchers, students and companies that manages a double pair of systems: a production cloud-HPC system and its twin dedicated to development. The cloud-HPC system is implemented thanks to an extended version of the GARR cloud (OpenStack) and the SLURM workload manager. HPC4AI is specifically designed to support system software development and cloud-HPC convergence tools. Among these streamflow (WMS), jupyter-as-a-service (SaaS), portable-secure-tenant (PasS). The experience gained in the design and management of HPC4AI forms the heart of the design of the livinglab of the Turin “FutureHPC” spoke of the National Center “HPC, BigData and Quantum Computing” funded by the PNRR which should be operational from September 2022.

@Misc{22:garr,
OPTkey = {},
author = {Marco Aldinucci},
title = {Da {HPC4AI} al living lab dello spoke {FutureHPC} del Centro Nazionale {HPC}},
howpublished = {Condivisioni, Conferenza GARR 2022},
month = {May},
year = {2022},
keywords = {keynote, hpc4ai, across, eupex, across, admire, textarossa, eumaster4hpc, icsc},
address = {Palermo, Italy},
Note ={Keynote talk},
OPTannote = {},
abstract = {HPC4AI is an open-access laboratory of the University of Turin open to researchers, students and companies that manages a double pair of systems: a production cloud-HPC system and its twin dedicated to development. The cloud-HPC system is implemented thanks to an extended version of the GARR cloud (OpenStack) and the SLURM workload manager. HPC4AI is specifically designed to support system software development and cloud-HPC convergence tools. Among these streamflow (WMS), jupyter-as-a-service (SaaS), portable-secure-tenant (PasS). The experience gained in the design and management of HPC4AI forms the heart of the design of the livinglab of the Turin "FutureHPC" spoke of the National Center "HPC, BigData and Quantum Computing" funded by the PNRR which should be operational from September 2022.},
url = {https://datacloud.di.unito.it/index.php/s/P3KSroSSmrRxZMc}
}

I. Colonnelli, Distributed workflows with Jupyter, J on The Beach 2022 Malaga, Spain: , Workshop, April, 2022.
[BibTeX] [Download PDF]

@Misc{22:jotb22-workshop,
OPTkey = {},
author = {Iacopo Colonnelli},
title = {Distributed workflows with {J}upyter},
howpublished = {J on The Beach 2022},
month = {April},
year = {2022},
keywords = {jupyter-workflow, deephealth, across},
address = {Malaga, Spain},
OPTannote = {},
note = {Workshop},
url = {https://datacloud.di.unito.it/index.php/s/om89q55S6ePf2Ji}
}

I. Colonnelli and D. Tranchitella, OpenDeepHealth: Crafting a Deep Learning Platform as a Service with Kubernetes, J on The Beach 2022 Malaga, Spain: , April, 2022.
[BibTeX] [Download PDF]

@Misc{22:jotb22,
OPTkey = {},
author = {Iacopo Colonnelli and Dario Tranchitella},
title = {{OpenDeepHealth}: Crafting a Deep Learning Platform as a Service with {K}ubernetes},
howpublished = {J on The Beach 2022},
month = {April},
year = {2022},
keywords = {streamflow, jupyter-workflow, across, deephealth, hpc4ai},
address = {Malaga, Spain},
OPTannote = {},
url = {https://datacloud.di.unito.it/index.php/s/n6J7STNnwdyqtET}
}

I. Colonnelli, StreamFlow: A framework for hybrid workflows, EUPEX WP5 bi-weekly meeting Virtual event: , April, 2022.
[BibTeX] [Download PDF]

@Misc{22:eupex-streamflow,
OPTkey = {},
author = {Iacopo Colonnelli},
title = {{StreamFlow}: A framework for hybrid workflows},
howpublished = {EUPEX WP5 bi-weekly meeting},
month = {April},
year = {2022},
keywords = {streamflow, eupex},
address = {Virtual event},
OPTannote = {},
url = {https://datacloud.di.unito.it/index.php/s/NjKEySP7HfrCQHZ}
}

@Misc{22:lincei,
OPTkey = {},
author = {Marco Aldinucci},
address = {Virtual event},
title = {Parallel Computing: a simple introduction, indeed an abstraction},
howpublished = {I Lincei per la Scuola: scienze per l'innovazione digitale},
month = {March},
year = {2022},
keywords = {invited, misc},
note = {Invited talk},
url = {https://datacloud.di.unito.it/index.php/s/nmaESJrapo2NqHq},
OPTannote = {}
}

I. Colonnelli, StreamFlow: A framework for hybrid workflows, ACROSS WP4 meeting Virtual event: , February, 2022.
[BibTeX] [Download PDF]

@Misc{22:across-streamflow,
OPTkey = {},
author = {Iacopo Colonnelli},
title = {{StreamFlow}: A framework for hybrid workflows},
howpublished = {ACROSS WP4 meeting},
month = {February},
year = {2022},
keywords = {streamflow, across},
address = {Virtual event},
OPTannote = {},
url = {https://datacloud.di.unito.it/index.php/s/FXFTKtQSRf6anMX}
}

B. Casella, Transfer Learning via Test-Time Neural Networks Aggregation, VISAPP Virtual event: , Feb, 2022.
[BibTeX] [Abstract] [Download PDF]

Presentation of the paper “Transfer Learning via Test-Time Neural Networks Aggregation” to VISAPP, the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

@misc{22:visapp,
abstract = {Presentation of the paper "Transfer Learning via Test-Time Neural Networks Aggregation" to VISAPP, the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications},
author = {Bruno Casella},
title = {Transfer Learning via Test-Time Neural Networks Aggregation
},
howpublished = {VISAPP},
month = {Feb},
year = {2022},
address = {Virtual event},
keywords = {},
annote = {},
url ={https://datacloud.di.unito.it/index.php/s/AKMT87eZDFX46JJ},
}

I. Colonnelli, The OpenDeepHealth toolkit, DeepHealth Winter School Torino, Italy: , January, 2022.
[BibTeX] [Download PDF]

@misc{22:DHWinterSchool,
address = {Torino, Italy},
author = {Iacopo Colonnelli},
howpublished = {DeepHealth Winter School},
keywords = {deephealth, hpc4ai},
month = {January},
title = {The {OpenDeepHealth} toolkit},
url = {https://datacloud.di.unito.it/index.php/s/cJ8pRNsWRrfwPqr},
year = {2022},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/cJ8pRNsWRrfwPqr}
}

B. Casella, Poster Session VISIGRAPP 2022, VISIGRAPP 2022 Torino, Italy: , Session chair, 2022.
[BibTeX]

@misc{22:VISIGRAPP,
address = {Torino, Italy},
author = {Bruno Casella},
date-added = {2022-02-07 14:00:00 +0100},
howpublished = {VISIGRAPP 2022},
keywords = {invited, misc},
month = feb,
note = {Session chair},
title = {Poster Session VISIGRAPP 2022},
year = {2022},
bdsk-url-1 = {}
}

M. Aldinucci and S. Rabellino, , Vertiv keep it running tour Milano, Italy: , Invited talk, November, 2021.
[BibTeX] [Download PDF]

@Misc{21:vertiv,
OPTkey = {},
author = {Marco Aldinucci and Sergiuo Rabellino},
OPTtitle = {HPC4AI Green Datacenter Design},
howpublished = {Vertiv keep it running tour},
month = {November},
year = {2021},
keywords = {invited, hpc4ai},
address = {Milano, Italy},
note = {Invited talk},
OPTannote = {},
url = {https://datacloud.di.unito.it/index.php/s/y6afrJr9w2DTmRN}
}

I. Colonnelli, StreamFlow: A framework for hybrid workflows, ACROSS WP4 meeting Virtual event: , October, 2021.
[BibTeX] [Download PDF]

@Misc{21:across-streamflow,
OPTkey = {},
author = {Iacopo Colonnelli},
title = {{StreamFlow}: A framework for hybrid workflows},
howpublished = {ACROSS WP4 meeting},
month = {October},
year = {2021},
keywords = {streamflow, across},
address = {Virtual event},
OPTannote = {},
url = {https://datacloud.di.unito.it/index.php/s/yrGYJL6CyNywF8a}
}

Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g., clouds, supercomputers, and both of them. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments (such as Kubernetes and SLURM), making it possible to execute onto multiple sites not sharing a common data space. Streamflow clearly distinguishes it from many other workflow management systems because it decouples the data dependencies from the deployment of (containerized) workflow steps. Streamflow also leverages CAPIO (Cross-Application Programmable I/O) to move data from one step to another efficiently. CAPIO captures the POSIX file system and streams it in parallel and in-memory to the workflow’s next step, possibly enabling in-transit data filtering.

@misc{21:eni:streamflow,
abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g., clouds, supercomputers, and both of them. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments (such as Kubernetes and SLURM), making it possible to execute onto multiple sites not sharing a common data space. Streamflow clearly distinguishes it from many other workflow management systems because it decouples the data dependencies from the deployment of (containerized) workflow steps. Streamflow also leverages CAPIO (Cross-Application Programmable I/O) to move data from one step to another efficiently. CAPIO captures the POSIX file system and streams it in parallel and in-memory to the workflow's next step, possibly enabling in-transit data filtering.},
address = {Virtual event},
author = {Marco Aldinucci},
howpublished = {Fifth EAGE Workshop on High Performance Computing for Upstream},
keywords = {keynote, streamflow, deephealth, across, admire},
month = {September},
note = {Keynote talk},
title = {The modernization of {HPC} applications for the cloud era},
year = {2021}}

I. Colonnelli, HPC Containers, ACROSS WP4 meeting Virtual event: , July, 2021.
[BibTeX] [Download PDF]

@Misc{21:across-containers,
OPTkey = {},
author = {Iacopo Colonnelli},
title = {{HPC} Containers},
howpublished = {ACROSS WP4 meeting},
month = {July},
year = {2021},
keywords = {across},
address = {Virtual event},
OPTannote = {},
url = {https://datacloud.di.unito.it/index.php/s/ddf3YBjpm8KBGAF}
}

M. Aldinucci, Reproducibility in the AI era, Penta Scientific Meeting Virtual event: , July, 2021.
[BibTeX] [Abstract] [Download PDF]

TBD

@misc{21:penta:covid,
abstract = {TBD},
address = {Virtual event},
author = {Marco Aldinucci},
howpublished = {Penta Scientific Meeting},
keywords = {invited, deephealth, across, admire},
month = {July},
title = {Reproducibility in the {AI} era},
url = {https://datacloud.di.unito.it/index.php/s/GLpf7kKSJRH733A},
year = {2021},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/s/GLpf7kKSJRH733A}}

M. Aldinucci, The Italian research on HPC key technologies across EuroHPC Virtual Conference, Italy: ACM, may, 2021.
[BibTeX] [Abstract] [Download PDF]

High-Performance Computing (HPC) is one of the strategic priorities for research and innovation worldwide due to its relevance for industrial and scientific applications. We envision HPC as composed of three pillars: infrastructures, applications, and key technologies and tools. While infrastructures are by construction centralized in large-scale HPC centers, and applications are generally within the purview of domain-specific organizations, key technologies fall in an intermediate case where coordination is needed, but design and development are often decentralized. A large group of Italian researchers has started a dedicated laboratory within the National Interuniversity Consortium for Informatics (CINI) to address this challenge. The laboratory, albeit young, has managed to succeed in its first attempts to propose a coordinated approach to HPC research within the EuroHPC Joint Undertaking, participating in the calls 2019-20 to five successful proposals for an aggregate total cost of 95M Euro. In this paper, we outline the working group’s scope and goals and provide an overview of the five funded projects, which become fully operational in March 2021, and cover a selection of key technologies provided by the working group partners, highlighting their usage development within the projects.

@misc{21:CINI_acm_CF_talk,
abstract = {High-Performance Computing (HPC) is one of the strategic priorities for research and innovation worldwide due to its relevance for industrial and scientific applications. We envision HPC as composed of three pillars: infrastructures, applications, and key technologies and tools. While infrastructures are by construction centralized in large-scale HPC centers, and applications are generally within the purview of domain-specific organizations, key technologies fall in an intermediate case where coordination is needed, but design and development are often decentralized. A large group of Italian researchers has started a dedicated laboratory within the National Interuniversity Consortium for Informatics (CINI) to address this challenge. The laboratory, albeit young, has managed to succeed in its first attempts to propose a coordinated approach to HPC research within the EuroHPC Joint Undertaking, participating in the calls 2019-20 to five successful proposals for an aggregate total cost of 95M Euro. In this paper, we outline the working group's scope and goals and provide an overview of the five funded projects, which become fully operational in March 2021, and cover a selection of key technologies provided by the working group partners, highlighting their usage development within the projects.},
address = {Virtual Conference, Italy},
author = {Marco Aldinucci},
booktitle = {{ACM Computing Frontiers}},
keywords = {eurohpc, across, admire, textarossa, eupex, eupilot},
month = may,
publisher = {{ACM}},
title = {The {Italian} research on {HPC} key technologies across {EuroHPC}},
url = {https://datacloud.di.unito.it/index.php/s/3ZYmDbEm84rbB9k},
year = {2021},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/s/3ZYmDbEm84rbB9k}}

@misc{21:poloict_hpc4ai,
address = {Torino, Italy},
author = {Marco Aldinucci and Marco Beccuti},
date-added = {2021-08-01 16:15:57 +0200},
date-modified = {2021-08-01 16:19:11 +0200},
howpublished = {Reserach meeting of the PoloICT},
keywords = {hpc4ai},
month = apr,
title = {{HPC4AI}: Un sistema per la ricerca e l'innovazione dei servizi cloud per l'Intelligenza Artificiale},
url = {https://datacloud.di.unito.it/index.php/s/BXdXLzsisQwDLrK},
year = {2021}}

We’ll present a methodology to run DNN pipelines on hybrid cloud+HPC infrastructure. We’ll also define a “universal pipeline” for medical images. The pipeline can reproduce all state-of-the-art DNNs to diagnose COVID-19 pneumonia, which appeared in the literature during the first Italian lockdown and following months. We can run all of them (across cloud+HPC platforms) and compare their performance in terms of sensitivity and specificity to set a baseline to evaluate future progress in the automated diagnosis of COVID-19. Also, the pipeline makes existing DNNs explainable by way of adversarial training. The pipeline is easily portable and can run across different infrastructures, adapting the performance-urgency trade-off. The methodology builds onto two novel software programs: the streamflow workflow system and the AI-sandbox concept (parallel container with user-space encrypted file system). We reach over 92\% accuracy in diagnosing COVID pneumonia.

@misc{21:gtc:clairecovid,
abstract = {We'll present a methodology to run DNN pipelines on hybrid cloud+HPC infrastructure. We'll also define a "universal pipeline" for medical images. The pipeline can reproduce all state-of-the-art DNNs to diagnose COVID-19 pneumonia, which appeared in the literature during the first Italian lockdown and following months. We can run all of them (across cloud+HPC platforms) and compare their performance in terms of sensitivity and specificity to set a baseline to evaluate future progress in the automated diagnosis of COVID-19. Also, the pipeline makes existing DNNs explainable by way of adversarial training. The pipeline is easily portable and can run across different infrastructures, adapting the performance-urgency trade-off. The methodology builds onto two novel software programs: the streamflow workflow system and the AI-sandbox concept (parallel container with user-space encrypted file system). We reach over 92\% accuracy in diagnosing COVID pneumonia.},
address = {Virtual event},
author = {Marco Aldinucci and Iacopo Colonnelli},
howpublished = {NVidia GTC'21},
keywords = {invited, streamflow, deephealth, hpc4ai},
month = {April},
note = {Invited talk},
title = {The Universal Cloud-{HPC} Pipeline for the {AI}-Assisted Explainable Diagnosis of {COVID-19} Pneumonia},
url = {https://datacloud.di.unito.it/index.php/s/AkQLbPpEEtDzbbm},
year = {2021},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/s/AkQLbPpEEtDzbbm}}

I. Colonnelli, StreamFlow: cross breeding cloud with HPC, 2021 CWL Mini Conference Virtual event: , Invited talk, February, 2021.
[BibTeX] [Abstract] [Download PDF]

Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space.

@misc{21:CWLMiniConference,
abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space.},
address = {Virtual event},
author = {Iacopo Colonnelli},
howpublished = {2021 CWL Mini Conference},
keywords = {invited, streamflow, deephealth},
month = {February},
note = {Invited talk},
title = {{StreamFlow}: cross breeding cloud with {HPC}},
url = {https://datacloud.di.unito.it/index.php/s/Le9gg4PfjRxBwXD},
year = {2021}
}

M. Aldinucci, On HPC, AI and their Fatal Attraction, CNR IEIIT, Thursday seminars (11 Feb 2021) Virtual event: , Invited talk, feb, 2021.
[BibTeX] [Download PDF]

@misc{21:CNR:hpcai,
address = {Virtual event},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {CNR IEIIT, Thursday seminars (11 Feb 2021)},
keywords = {invited, hpc4ai, deephealth},
month = feb,
note = {Invited talk},
title = {On {HPC}, {AI} and their Fatal Attraction},
url = {https://datacloud.di.unito.it/index.php/s/pSDxNPncic8gEy8},
year = {2021},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2021_CNR_HPC+AI.pdf}}

M. Aldinucci, HPC application cloudification: the streamflow toolkit, PARMA-DITAM (co-localed with HiPEAC) Virtual event: , Keynote talk, January, 2021.
[BibTeX] [Download PDF]

@misc{21:parmaditam:hpc4ai,
address = {Virtual event},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {PARMA-DITAM (co-localed with HiPEAC)},
keywords = {keynote, hpc4ai, deephealth},
month = {January},
title = {{HPC} application cloudification: the streamflow toolkit},
url = {https://datacloud.di.unito.it/index.php/s/HWZijXPqmwfoYCp},
year = {2021},
note = {Keynote talk},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2021_PARMA-DITAM_keynote_HPC-Cloudification.pdf}}

M. Aldinucci, Lung Nodules Segmentation in CT scans by DeepHealth toolkit, 25th Intl. Conference on Pattern Recognition Milano. Italy: BDVA, Demo, jan, 2021.
[BibTeX] [Download PDF]

@misc{21:icpr:demodeephealth,
address = {Milano. Italy},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {25th Intl. Conference on Pattern Recognition},
keywords = {demo, deephealth},
month = jan,
note = {Demo},
publisher = {{BDVA}},
title = {Lung Nodules Segmentation in {CT} scans by DeepHealth toolkit},
url = {https://datacloud.di.unito.it/index.php/s/KYJMcT3pfpat2Hx},
year = {2021},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2021-ICPR20-S1.1-LungNodulesSegmentation-Presentation.pdf}}

Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments and that makes it possible to execute multiple sites not sharing a common data space. StreamFlow supports both task and data parallelism and enables the reproducible and scalable execution of workflows, such as AI pipelines, in hybrid cloud-HPC environments. As a running example, we use the novel “universal COVID-19 pipeline” that explore the whole optimisation space of the training of different DNNs to classify COVID-19 lung lesions.

@misc{21:hlpp:streamflow,
abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments and that makes it possible to execute multiple sites not sharing a common data space. StreamFlow supports both task and data parallelism and enables the reproducible and scalable execution of workflows, such as AI pipelines, in hybrid cloud-HPC environments. As a running example, we use the novel ``universal COVID-19 pipeline'' that explore the whole optimisation space of the training of different DNNs to classify COVID-19 lung lesions.},
address = {Virtual event},
author = {Marco Aldinucci},
howpublished = {14th Intl. Symposium on High-Level Programming and Applications (HLPP)},
keywords = {keynote, streamflow, deephealth, across, admire},
month = jul,
title = {From skeletons to workflows in the cloud-edge era},
url = {https://datacloud.di.unito.it/index.php/s/RyRPjNBse5PKnab},
year = {2021},
note ={Keynote talk},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/s/RyRPjNBse5PKnab}}

M. Aldinucci and M. Beccuti, DeepHealth: Deep Learning ad alte prestazioni per applicazioni in ambito medico, Reserach meeting of the PoloICT Torino, Italy: , 2021.
[BibTeX] [Download PDF]

@misc{21:poloict:deephealth,
address = {Torino, Italy},
author = {Marco Aldinucci and Marco Beccuti},
date-added = {2021-08-01 16:19:46 +0200},
date-modified = {2021-08-01 16:20:45 +0200},
howpublished = {Reserach meeting of the PoloICT},
keywords = {deephealth, hpc4ai},
month = apr,
title = {{DeepHealth}: Deep Learning ad alte prestazioni per applicazioni in ambito medico},
url = {https://datacloud.di.unito.it/index.php/s/2F5Net5HdfJTysa},
year = {2021},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/s/BXdXLzsisQwDLrK}}

M. Aldinucci, DeepHealth perspective, Future challenges in IoT, AI, and convergence of HPC & Cloud & Big Data – BDVA Data Week Virtual event: , 2021.
[BibTeX] [Abstract]

TBD

@misc{21:dataweek:deephealth,
abstract = {TBD},
address = {Virtual event},
author = {Marco Aldinucci},
howpublished = {Future challenges in IoT, AI, and convergence of HPC & Cloud & Big Data -- BDVA Data Week},
keywords = {invited, deephealth},
month = may,
title = {DeepHealth perspective},
year = {2021}}

M. Aldinucci, HPC4AI: A cloud-HPC ecosystem designed for research and innovation, Advanced Computing Workshop 2021: HPC and Beyond , Invited talk, 2021.
[BibTeX] [Abstract] [Download PDF]

The University of Turin and Polytechnic University of Turin have joined forces to create a federated competence centre on High-Performance Computing (HPC), Artificial Intelligence (AI) and Big Data Analytics (BDA). HPC4AI is designed as a centre capable of collaborating with entrepreneurs to boost their ability to innovate on data-driven technologies and applications. HPC4AI started in 2017 with the construction of four new federated computing laboratories completed at the end of 2020. HPC4AI is organized in two poles: one at the University of Turin (UNITO), which coordinated the design and implementation phase of HPC4AI, and another at the Polytechnic of Turin (POLITO).

@misc{20:dell:hpc4ai,
abstract = {The University of Turin and Polytechnic University of Turin have joined forces to create a federated competence centre on High-Performance Computing (HPC), Artificial Intelligence (AI) and Big Data Analytics (BDA). HPC4AI is designed as a centre capable of collaborating with entrepreneurs to boost their ability to innovate on data-driven technologies and applications.
HPC4AI started in 2017 with the construction of four new federated computing laboratories completed at the end of 2020. HPC4AI is organized in two poles: one at the University of Turin (UNITO), which coordinated the design and implementation phase of HPC4AI, and another at the Polytechnic of Turin (POLITO).
},
author = {Marco Aldinucci},
date-added = {2021-01-21 13:07:40 +0000},
date-modified = {2021-01-21 13:12:29 +0000},
howpublished = {Advanced Computing Workshop 2021: HPC and Beyond},
keywords = {invited, hpc4ai},
month = jan,
note = {Invited talk},
title = {{HPC4AI}: A cloud-{HPC} ecosystem designed for research and innovation},
url = {https://datacloud.di.unito.it/index.php/s/NmM9kB42pJZGMx6},
year = {2021},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/s/NmM9kB42pJZGMx6}}

I. Colonnelli and S. Rabellino, JupyterFlow: Jupyter Notebooks su larga scala, Workshop GARR 2020 Virtual event: , November, 2020.
[BibTeX] [Abstract] [Download PDF]

I Jupyter Notebook sono largamente utilizzati sia in ambito industriale che accademico come strumento di didattica, prototipazione e analisi esplorative. Purtroppo il sistema runtime standard di Jupyter non è abbastanza potente per sostenere un carichi di lavoro reali e spesso l’unica soluzione è quella di riscrivere il codice da zero in una tecnologia con supporto HPC. Intrgrando lo stack Jupyter con StreamFlow (https://streamflow.di.unito.it/) è possibile creare i Notebook tramite un’interfaccia web su cloud ed eseguirli in maniera trasparente in remoto su una VM con GPU o su nodi HPC.

@misc{20:GarrWorkshop,
abstract = {I Jupyter Notebook sono largamente utilizzati sia in ambito industriale che accademico come strumento di didattica, prototipazione e analisi esplorative. Purtroppo il sistema runtime standard di Jupyter non \`{e} abbastanza potente per sostenere un carichi di lavoro reali e spesso l'unica soluzione \`{e} quella di riscrivere il codice da zero in una tecnologia con supporto HPC. Intrgrando lo stack Jupyter con StreamFlow (https://streamflow.di.unito.it/) \`{e} possibile creare i Notebook tramite un'interfaccia web su cloud ed eseguirli in maniera trasparente in remoto su una VM con GPU o su nodi HPC.},
address = {Virtual event},
author = {Iacopo Colonnelli and Sergio Rabellino},
howpublished = {Workshop GARR 2020},
keywords = {jupyter-workflow, hpc4ai, deephealth},
month = {November},
title = {{JupyterFlow}: {J}upyter {N}otebooks su larga scala},
url = {https://datacloud.di.unito.it/index.php/s/ASPEmyXAj5QscgC},
year = {2020},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/ASPEmyXAj5QscgC},
bdsk-url-2 = {https://www.eventi.garr.it/it/ws20/programma/speaker/680-iacopo-colonnelli}
}

I. Colonnelli, StreamFlow: cross breeding cloud with HPC, HPC-Europa3 2nd Transnational Access Meeting (TAM) Virtual event: , Invited talk, October, 2020.
[BibTeX] [Abstract] [Download PDF]

Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space. StreamFlow is then exemplified on a novel bioinformatics pipeline for single-cell transcriptomic data analysis workflow.

@misc{20:HPCEuropa3TAM,
abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space. StreamFlow is then exemplified on a novel bioinformatics pipeline for single-cell transcriptomic data analysis workflow.},
address = {Virtual event},
author = {Iacopo Colonnelli},
howpublished = {HPC-Europa3 2nd Transnational Access Meeting (TAM)},
keywords = {invited, streamflow},
month = {October},
note = {Invited talk},
title = {{StreamFlow}: cross breeding cloud with {HPC}},
url = {https://datacloud.di.unito.it/index.php/s/qPHHrSNxk8QXJDw},
year = {2020},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/qPHHrSNxk8QXJDw},
bdsk-utl-2 = {https://drive.google.com/file/d/1aTVhlsrS7R2FzEWRTTtm4Mzr7O8R4Jq1/view}
}

M. Aldinucci, Building avenues for AI-assisted diagnosis over the bridge from HPC to AI, CLAIRE COVID webinar Torino, Italy: , Invited talk, jul, 2020.
[BibTeX] [Download PDF]

@misc{20:claire:taskforce,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {CLAIRE COVID webinar},
keywords = {invited, deephealth, claire},
month = jul,
note = {Invited talk},
title = {Building avenues for AI-assisted diagnosis over the bridge from {HPC} to {AI}},
url = {https://datacloud.di.unito.it/index.php/s/RqpNCHyyL6wc5ds},
year = {2020},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2020_CLAIRE-Medical-Images-Taskforce-webinar.pdf}}

@misc{20:bdva:deephealth,
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-02-14 18:48:35 +0100},
howpublished = {HPC, Big Data, IoT and AI future industry-driven collaborative strategic topics virtual workshop --- HPC / HPDA spectrum},
keywords = {invited, deephealth},
month = jul,
note = {Invited talk},
publisher = {BDVA},
title = {The DeepHealth project},
url = {https://datacloud.di.unito.it/index.php/s/ortwLJHS2q96irb},
year = {2020},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2020_DEEPHEALTH_ICT-11-projects-workshop-v3.0.pdf}}

M. Aldinucci, StreamFlow: cross-breeding cloud with HPC, Computability in Europe 2020 (CIE) , Invited talk, jun, 2020.
[BibTeX] [Download PDF]

@misc{20:CIE:streamflow,
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {Computability in Europe 2020 (CIE)},
keywords = {invited, deephealth},
month = jun,
note = {Invited talk},
title = {StreamFlow: cross-breeding cloud with {HPC}},
url = {https://datacloud.di.unito.it/index.php/s/Ltqo4SmJj42wjyo},
year = {2020},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2020_streamflow-CIE.pdf}}

@misc{20:chem:HPCAI,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {Machine Learning Meets Chemistry @ the Department of Chemistry, University of Torino},
keywords = {invited, hpc4ai, deephealth},
month = feb,
note = {Invited talk},
title = {Machine Learning: the treacherous journey from data to knowledge (with examples from {HPC4AI@UNITO} platform)},
url = {https://datacloud.di.unito.it/index.php/s/ffyZYYqNQpkza4F},
year = {2020},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2020_ML-chemistry.pdf}}

M. Aldinucci, HPC4AI: From Enabling Platforms to Technology Sovereignty to Innovation, Elixir-Italia meeting Torino, Italy: , Invited talk, February, 2020.
[BibTeX] [Download PDF]

@misc{20:elixir:hpc4ai,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {Elixir-Italia meeting},
keywords = {invited, hpc4ai},
month = {February},
note = {Invited talk},
title = {{HPC4AI}: From Enabling Platforms to Technology Sovereignty to Innovation},
url = {https://datacloud.di.unito.it/index.php/s/g6LZiErXH4PPRCj},
year = {2020},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2020_02_Elixir_HPC4AI.pdf}}

M. Aldinucci, Mnemocomputing, HLPGPU 2019 (Satellite workshop of HiPEAC 2019) Valencia, Spain: , Keynote talk, jan, 2020.
[BibTeX] [Download PDF]

@misc{20:hlpgpu:mnemocomputing,
address = {Valencia, Spain},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {HLPGPU 2019 (Satellite workshop of HiPEAC 2019)},
keywords = {keynote},
note ={Keynote talk},
month = jan,
title = {Mnemocomputing},
url = {https://datacloud.di.unito.it/index.php/s/8pAGGsw4nEtrtNj},
year = {2020},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2020_HLPGPU_keynote.pdf}}

M. Aldinucci, The Joint Undertaking EuroHPC, Giornata Nazionale di Lancio dei bandi Information and Communication Technologies in Horizon 2020 Roma, Italy: , Invited talk, oct, 2019.
[BibTeX]

@misc{19:APRE:EuroHPC,
address = {Roma, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-17 14:00:00 +0100},
date-modified = {2021-03-17 14:00:00 +0100},
howpublished = {Giornata Nazionale di Lancio dei bandi Information and Communication Technologies in Horizon 2020},
keywords = {invited, misc},
month = oct,
note = {Invited talk},
title = {The Joint Undertaking EuroHPC},
year = {2019},
Bdsk-Url-1 = {}}

I. Colonnelli, StreamFlow: un approccio dichiarativo a workflow e pipeline di micro-servizi, Workshop GARR 2019 Roma, Italy: , October, 2019.
[BibTeX] [Abstract] [Download PDF]

Negli ultimi anni, gli approcci orientati ai container si sono dimostrati particolarmente efficaci nel garantire portabilità e riproducibilità dei workflow scientifici. Tuttavia, con il continuo aumento del volume di dati a disposizione e la crescente complessità delle procedure di analisi in ogni campo della ricerca, anche i requisiti di performance e riusabilità si fanno via via sempre più essenziali. L’obiettivo principale di StreamFlow è quello di fornire un nuovo paradigma, totalmente dichiarativo, per la descrizione e l’accelerazione di workflow scientifici in ambienti distribuiti. La peculiarità di StreamFlow risiede nel fatto che l’ambiente di esecuzione è interamente descritto in termini di servizi (container), connessioni tra essi e fattori di replica. Inoltre, ogni task del workflow è esplicitamente mappato sulla tipologia di servizio richiesta. Questo permette un maggior controllo sull’utilizzo delle risorse e politiche di scheduling più precise, a vantaggio delle performance. I principali vantaggi di un approccio dichiarativo sono invece la più facile comprensione ed estensione dei modelli esistenti, a vantaggio della riusabilitià.

@misc{19:GarrWorkshop,
abstract = {Negli ultimi anni, gli approcci orientati ai container si sono dimostrati particolarmente efficaci nel garantire portabilit\`{a} e riproducibilit\`{a} dei workflow scientifici. Tuttavia, con il continuo aumento del volume di dati a disposizione e la crescente complessit\`{a} delle procedure di analisi in ogni campo della ricerca, anche i requisiti di performance e riusabilit\`{a} si fanno via via sempre pi\`{u} essenziali. L'obiettivo principale di StreamFlow \`{e} quello di fornire un nuovo paradigma, totalmente dichiarativo, per la descrizione e l'accelerazione di workflow scientifici in ambienti distribuiti. La peculiarit\`{a} di StreamFlow risiede nel fatto che l'ambiente di esecuzione \`{e} interamente descritto in termini di servizi (container), connessioni tra essi e fattori di replica. Inoltre, ogni task del workflow \`{e} esplicitamente mappato sulla tipologia di servizio richiesta. Questo permette un maggior controllo sull'utilizzo delle risorse e politiche di scheduling pi\`{u} precise, a vantaggio delle performance. I principali vantaggi di un approccio dichiarativo sono invece la pi\`{u} facile comprensione ed estensione dei modelli esistenti, a vantaggio della riusabiliti\`{a}.},
address = {Roma, Italy},
author = {Iacopo Colonnelli},
howpublished = {Workshop GARR 2019},
keywords = {streamflow},
month = {October},
title = {{StreamFlow}: un approccio dichiarativo a workflow e pipeline di micro-servizi},
url = {https://datacloud.di.unito.it/index.php/s/kZqyiQnBEQNdXJe},
year = {2019},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/kZqyiQnBEQNdXJe},
bdsk-url-2 = {https://www.eventi.garr.it/it/ws19/programma/speaker/580-iacopo-colonnelli}
}

M. Aldinucci, HPC4AI, an on-demand federated platform endeavour, Ospedale San Raffaele Milano, Italy: , Invited talk, may, 2019.
[BibTeX] [Download PDF]

@misc{19:SR:hpc4ai,
address = {Milano, Italy},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {Ospedale San Raffaele},
keywords = {invited, hpc4ai},
month = may,
note = {Invited talk},
title = {{HPC4AI}, an on-demand federated platform endeavour},
url = {https://datacloud.di.unito.it/index.php/s/P7fxgExkJDAFQbm},
year = {2019},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2019_SanRaffaele.pdf}}

@misc{19:conference:PublicBD,
address = {Turin, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-17 14:00:00 +0100},
date-modified = {2021-03-17 14:00:00 +0100},
howpublished = {Convegno ``L'amministrazione pubblica con i Big data''},
keywords = {invited, misc},
month = may,
note = {Invited talk},
title = {L'infrastruttura necessaria per creare interoperabilit{\`a} tra pubbliche amministrazioni},
url = {https://datacloud.di.unito.it/index.php/s/KPebr76m88jQATi},
year = {2019},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2019_Convegno_CloudPA_Aldinucci.pdf}}

M. Aldinucci, High-Performance Computing, Istituto per la Competitività Roma, Italy: , Invited talk, apr, 2019.
[BibTeX] [Download PDF]

@misc{19:iCom:HPC,
address = {Roma, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-17 15:00:00 +0100},
date-modified = {2021-03-17 15:00:00 +0100},
howpublished = {Istituto per la Competitivit{\`a}},
keywords = {invited, misc},
month = apr,
note = {Invited talk},
title = {High-Performance Computing},
url = {https://www.i-com.it/2019/02/02/supercomputer-hpe-computing/},
year = {2019},
Bdsk-Url-1 = {}}

M. Aldinucci, The evolution of high-performance systems: from HPC to Big Data to Deep Learning, 4th Open SmartData@PoliTO Workshop Torino, Italy: , Invited talk, feb, 2019.
[BibTeX] [Download PDF]

@misc{19:SmartData:fromHPCtoDL,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-17 15:00:00 +0100},
date-modified = {2021-03-17 15:00:00 +0100},
howpublished = {4th Open SmartData@PoliTO Workshop},
keywords = {invited, misc},
month = feb,
note = {Invited talk},
title = {The evolution of high-performance systems: from HPC to Big Data to Deep Learning},
url = {https://datacloud.di.unito.it/index.php/s/cZitMsp5GPJ2Qzf},
year = {2019},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2019_smartdata_polito.pdf}}

The wavefront pattern captures the unfolding of a parallel computation in which data elements are laid out as a logical multidimensional grid and the dependency graph favours a diagonal sweep across the grid. In the emerging area of spectral graph analysis, the computing often consists in a wavefront running over a tiled matrix, involving expensive linear algebra kernels. While these applications might benefit from parallel heterogeneous platforms (multi-core with GPUs),programming wavefront applications directly with high-performance linear algebra libraries yields code that is complex to write and optimize for the specific application. We advocate a methodology based on two abstractions (linear algebra and parallel pattern-based run-time), that allows to develop portable, self-configuring, and easy-to-profile code on hybrid platforms.

@misc{19:PDPArmadillo,
abstract = {The wavefront pattern captures the unfolding of a parallel computation in which data elements are laid out as a logical multidimensional grid and the dependency graph favours a diagonal sweep across the grid. In the emerging area of spectral graph analysis, the computing often consists in a wavefront running over a tiled matrix, involving expensive linear algebra kernels. While these applications might benefit from parallel heterogeneous platforms (multi-core with GPUs),programming wavefront applications directly with high-performance linear algebra libraries yields code that is complex to write and optimize for the specific application. We advocate a methodology based on two abstractions (linear algebra and parallel pattern-based run-time), that allows to develop portable, self-configuring, and easy-to-profile code on hybrid platforms.},
address = {Pavia, Italy},
author = {Iacopo Colonnelli},
howpublished = {27th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP 2019)},
publisher = {{IEEE}},
keywords = {misc},
month = {February},
title = {Accelerating spectral graph analysis through wavefronts of linear algebra operations},
url = {https://datacloud.di.unito.it/index.php/s/zK4eSzdsdB8CfQX},
year = {2019},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/zK4eSzdsdB8CfQX}
}

This work presents a novel approach to distributed training of deep neural networks (DNNs) that aims to overcome the issues related to mainstream approaches to data parallel training. Established techniques for data parallel training are discussed from both a parallel computing and deep learning perspective, then a different approach is presented that is meant to allow DNN training to scale while retaining good convergence properties. Moreover, an experimental implementation is presented as well as some preliminary results.

@misc{19:PDPNNT,
abstract = {This work presents a novel approach to distributed training of deep neural networks (DNNs) that aims to overcome the issues related to mainstream approaches to data parallel training. Established techniques for data parallel training are discussed from both a parallel computing and deep learning perspective, then a different approach is presented that is meant to allow DNN training to scale while retaining good convergence properties. Moreover, an experimental implementation is presented as well as some preliminary results.},
address = {Pavia, Italy},
author = {Iacopo Colonnelli},
howpublished = {27th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP 2019)},
publisher = {{IEEE}},
keywords = {misc},
month = {February},
title = {Deep Learning at Scale},
url = {https://datacloud.di.unito.it/index.php/s/nRW9M69C3AtpDoM},
year = {2019},
bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/nRW9M69C3AtpDoM}
}

M. Aldinucci, Towards DeepLearning at Scale, SPPEXA Final Symposium Dresden, Germany: , Invited talk, 2019.
[BibTeX]

@misc{19:SPPEXA:DLscale,
address = {Dresden, Germany},
author = {Marco Aldinucci},
date-added = {2021-03-17 14:00:00 +0100},
date-modified = {2021-03-17 14:00:00 +0100},
howpublished = {SPPEXA Final Symposium},
keywords = {invited, misc},
month = oct,
note = {Invited talk},
title = {Towards DeepLearning at Scale},
year = {2019},
Bdsk-Url-1 = {}}

M. Aldinucci, How Artificial Intelligence is shaping the Future of Health, 1st Industrial Conference on Artificial Intelligence and health Milano, Italy: , Invited talk, 2019.
[BibTeX] [Download PDF]

@misc{19:ICAIH:AIhealth,
address = {Milano, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-16 15:00:00 +0100},
date-modified = {2021-03-16 15:00:00 +0100},
howpublished = {1st Industrial Conference on Artificial Intelligence and health},
keywords = {invited, misc},
month = nov,
note = {Invited talk},
title = {How Artificial Intelligence is shaping the Future of Health},
url = {https://datacloud.di.unito.it/index.php/s/FPDCjcWwprAa7D7},
year = {2019},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2019_ICAIH.pdf}}

@misc{18:ChimicaPP:fromHPCtoDL,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 12:00:00 +0100},
date-modified = {2021-03-18 12:00:00 +0100},
howpublished = {Chimica passione periodica: Big Data: Modelli predittivi, simulazione, analisi, Dipartimento di Chimica, Universit{\`a} di Torino},
keywords = {invited, misc},
month = nov,
note = {Invited talk},
title = {L'evoluzione delle piattaforme e dei sistemi ad alte prestazioni: da HPC ai Big Data al Deep Learning},
url = {https://datacloud.di.unito.it/index.php/s/sLxm6G2Ma5jj9BP},
year = {2018},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2018_chimica_passione_periodica_Aldinucci.pdf}}

@misc{18:CITYLAB:AIonDemand,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 12:00:00 +0100},
date-modified = {2021-03-18 12:00:00 +0100},
howpublished = {CITYLAB Ecosystem: i dati per le aziende, le citt{\`a} e le universit{\`a}, Nuvola Lavazza Torino},
keywords = {invited, misc},
month = jun,
note = {Invited talk},
title = {Le piattaforme AI-on-demand come fattore di l'innovazione nelle PMI},
url = {https://datacloud.di.unito.it/index.php/s/Twy9zwbJB9KnBE2},
year = {2018},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2018_chimica_passione_periodica_Aldinucci.pdf}}

M. Aldinucci, HPC4AI, an AI-on-demand federated platform endeavour, ACM Computing Frontiers Ischia, Italy: , Invited talk, may, 2018.
[BibTeX] [Download PDF]

@misc{18:ACM:HPC4AI,
address = {Ischia, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 12:00:00 +0100},
date-modified = {2021-03-18 12:00:00 +0100},
howpublished = {ACM Computing Frontiers},
keywords = {invited, misc},
month = may,
note = {Invited talk},
title = {HPC4AI, an AI-on-demand federated platform endeavour},
url = {https://datacloud.di.unito.it/index.php/s/NWbZ2FYpeYR47T6},
year = {2018},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2018_HPC4AI_ACM_CF.pdf}}

@misc{18:ARM:energyEfficiency,
address = {Cambridge, UK},
author = {Marco Aldinucci},
date-added = {2021-03-18 15:00:00 +0100},
date-modified = {2021-03-18 15:00:00 +0100},
howpublished = {Arm Research ltd},
keywords = {invited, misc},
month = mar,
note = {Invited talk},
title = {From mutexes to lock-free to atomic-less and back to (un-contended) mutex: A story in which energy efficiency is unexpectedly constantly increasing},
year = {2018},
Bdsk-Url-1 = {}}

M. Aldinucci, Designing a heterogeneous federated data center for research, DiSIA, University of Florence Firenze, Italy: , Invited talk, mar, 2018.
[BibTeX]

@misc{18:DISIA:federated,
address = {Firenze, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 15:00:00 +0100},
date-modified = {2021-03-18 15:00:00 +0100},
howpublished = {DiSIA, University of Florence},
keywords = {invited, misc},
month = mar,
note = {Invited talk},
title = {Designing a heterogeneous federated data center for research},
year = {2018},
Bdsk-Url-1 = {}}

M. Aldinucci, Toward Near-Data Processing service computing, Parallel, Distributed, and Network-Based Processing (PDP) Cambridge, UK: IEEE, Keynote talk, 2018.
[BibTeX] [Download PDF]

@misc{18:PDP:NDP,
address = {Cambridge, UK},
annote = {http://www.pdp2018.org/invited.html},
author = {Marco Aldinucci},
date-added = {2021-01-01 18:02:39 +0100},
date-modified = {2021-01-01 18:54:26 +0100},
howpublished = {Parallel, Distributed, and Network-Based Processing (PDP)},
keywords = {keynote},
note ={Keynote talk},
month = mar,
publisher = {{IEEE}},
title = {Toward Near-Data Processing service computing},
url = {https://datacloud.di.unito.it/index.php/s/ePDZ44KSsgXSGHw},
year = {2018},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2018_PDP_keynote.pdf}}

M. Aldinucci, OCCAM: Heterogeneous platforms mixed blessing of code optimisation, Riunione della Commissione Calcolo e Reti dell’INFN Torino, Italy: , Invited talk, sep, 2017.
[BibTeX] [Download PDF]

@misc{17:INFN:occam,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 16:00:00 +0100},
date-modified = {2021-03-18 16:00:00 +0100},
howpublished = {Riunione della Commissione Calcolo e Reti dell'INFN},
keywords = {invited, misc},
month = sep,
note = {Invited talk},
title = {OCCAM: Heterogeneous platforms mixed blessing of code optimisation},
url = {https://agenda.infn.it/login/?next=%2Fevent%2F12983%2Ftimetable%2F%3Fview%3Dstandard},
year = {2017},
Bdsk-Url-1 = {}}

M. Aldinucci, Designing a heterogeneous federated data center for research, Euro-Par 2017 Santiago di Compostela, Spain: , aug, 2017.
[BibTeX] [Download PDF]

@misc{17:EUROPAR:presentation,
address = {Santiago di Compostela, Spain},
author = {Marco Aldinucci},
date-added = {2021-03-18 15:00:00 +0100},
date-modified = {2021-03-18 15:00:00 +0100},
howpublished = {Euro-Par 2017},
keywords = {misc},
month = aug,
title = {Designing a heterogeneous federated data center for research},
url = {https://datacloud.di.unito.it/index.php/s/DyJ3A6Hgj52e33b},
year = {2017},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2017_Europar2018-presentation.pdf}}

@misc{17:autodasp:17,
address = {Santiago de Compostela, Spain},
annote = {http://calvados.di.unipi.it/auto-dasp-17/index.php/program/},
author = {Marco Aldinucci},
date-added = {2021-01-01 19:04:30 +0100},
date-modified = {2021-01-01 19:07:40 +0100},
howpublished = {International Workshop on Autonomic Solutions for Parallel and Distributed Data Stream Processing (Auto-DaSP 2017), Workshop of EuroPar 2017},
keywords = {keynote},
note ={Keynote talk},
month = aug,
title = {Streaming in the {PGAS} Era},
url = {https://datacloud.di.unito.it/index.php/s/H6ZRoK6gLZDQFta},
year = {2017},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2017_AutoDASP_keynote_pgas.pdf}}

M. Aldinucci, Partitioned Global Address Space in the mainstream of C++ programming, International Parallel Computing (ParCo) Bologna, Italy: , Keynote talk, 2017.
[BibTeX] [Download PDF]

@misc{20:parco:PGAS,
address = {Bologna, Italy},
annote = {http://www.parco.org/keynotes.html},
author = {Marco Aldinucci},
date-added = {2021-01-01 19:00:41 +0100},
date-modified = {2021-01-01 19:04:21 +0100},
howpublished = {International Parallel Computing (ParCo)},
keywords = {keynote},
note ={Keynote talk},
month = sep,
title = {Partitioned Global Address Space in the mainstream of {C++} programming},
url = {https://datacloud.di.unito.it/index.php/s/dTAm7oAfHRTY7BC},
year = {2017},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2017_PARCO_keynote.pdf}}

@misc{16:CARLOALBERTO:dataScience,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 16:00:00 +0100},
date-modified = {2021-03-18 16:00:00 +0100},
howpublished = {Workshop of the Competence Centre on Scientific Computing of University of Torino},
keywords = {invited, misc},
month = oct,
note = {Invited talk},
title = {HPC come piattaforma abilitante: rischi e opportunit{\`a}},
url = {https://datacloud.di.unito.it/index.php/s/6qgoQoSXaZpMQq4},
year = {2016},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2016_C3S_Aldinucci.pdf}}

M. Aldinucci, Atomic operations considered hARMful, ARM Research Summary Cambridge, UK: , sep, 2016.
[BibTeX] [Download PDF]

@misc{16:ARM:atomic,
address = {Cambridge, UK},
author = {Marco Aldinucci},
date-added = {2021-03-18 16:00:00 +0100},
date-modified = {2021-03-18 16:00:00 +0100},
howpublished = {ARM Research Summary},
keywords = {misc},
month = sep,
title = {Atomic operations considered hARMful},
url = {https://datacloud.di.unito.it/index.php/s/mBoWAmMzj8nYrbe},
year = {2016},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2016_C3S_Aldinucci.pdf}}

M. Aldinucci, I supercomputer venuti dal futuro: Exascale Computing, International Pint of Science, Officine ferroviarie Torino, Italy: , Invited talk, may, 2016.
[BibTeX]

@misc{16:PintOfScience:exascale,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 16:00:00 +0100},
date-modified = {2021-03-18 16:00:00 +0100},
howpublished = {International Pint of Science, Officine ferroviarie},
keywords = {invited, misc},
month = may,
note = {Invited talk},
title = {I supercomputer venuti dal futuro: Exascale Computing},
year = {2016},
Bdsk-Url-1 = {}}

@misc{16:SIAM:composition,
address = {Paris, France},
author = {Marco Aldinucci},
date-added = {2021-03-18 16:00:00 +0100},
date-modified = {2021-03-18 16:00:00 +0100},
howpublished = {SIAM Conference on Parallel Processing for Scientific Computing},
keywords = {misc},
month = apr,
title = {Composition and Compartmentalisation As Enabling Features for Data-Centric, Extreme Scale Applications: An MPI-X Approach},
url = {https://datacloud.di.unito.it/index.php/s/cWne6zEWypepo7H},
year = {2016},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2016_C3S_Aldinucci.pdf}}

M. Aldinucci, , FastData@UNITO opening Torino, Italy: , Invited talk, mar, 2016.
[BibTeX]

@misc{16:UNITO:fastdata,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 16:00:00 +0100},
date-modified = {2021-03-18 16:00:00 +0100},
howpublished = {FastData@UNITO opening},
keywords = {invited, misc},
month = mar,
note = {Invited talk},
year = {2016},
Bdsk-Url-1 = {}}

@misc{14:hpcc:datacentric,
address = {Paris, France},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-01-01 17:49:10 +0100},
howpublished = {6th IEEE Intl. Conference on High-Performance Computing and Communications (HPCC)},
keywords = {keynote},
note ={Keynote talk},
month = aug,
title = {Parallel patterns, data-centric concurrency, and heterogeneous computing},
url = {https://datacloud.di.unito.it/index.php/s/mRSTsB6qjKMYiEN},
year = {2014},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2014_HPCC_Aldinucci.pdf}}

M. Aldinucci, An Overview of FastFlow: Combining Pattern-Level Abstraction and Efficiency in GPGPUs, GPU Technology Conference (GTC 2014) San Jose, CA, USA: , mar, 2014.
[BibTeX] [Download PDF]

@misc{14:GTC:overview,
address = {San Jose, CA, USA},
author = {Marco Aldinucci},
date-added = {2021-03-18 16:00:00 +0100},
date-modified = {2021-03-18 16:00:00 +0100},
howpublished = {GPU Technology Conference (GTC 2014)},
keywords = {misc},
month = mar,
title = {An Overview of FastFlow: Combining Pattern-Level Abstraction and Efficiency in GPGPUs},
url = {https://datacloud.di.unito.it/index.php/s/sRYnknEHn3Qkj5b},
year = {2014},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2016_C3S_Aldinucci.pdf}}

M. Aldinucci, FastFlow: Combining Pattern-Level Abstraction and Efficiency in GPGPUs, GPU Technology Conference (GTC 2014) San Jose, CA, USA: , mar, 2014.
[BibTeX] [Download PDF]

@misc{14:GTC:fastflow,
address = {San Jose, CA, USA},
author = {Marco Aldinucci},
date-added = {2021-03-18 16:00:00 +0100},
date-modified = {2021-03-18 16:00:00 +0100},
howpublished = {GPU Technology Conference (GTC 2014)},
keywords = {misc},
month = mar,
title = {FastFlow: Combining Pattern-Level Abstraction and Efficiency in GPGPUs},
url = {https://datacloud.di.unito.it/index.php/s/ibCGJc7tJyASMbN},
year = {2014},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2014_S4585-Marco-Aldinucci.pdf}}

@misc{14:dublin:ff,
address = {Dublin, Ireland},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-02-01 11:32:48 +0100},
howpublished = {Intl. Summer School in Parallel Patterns},
keywords = {keynote},
note ={Keynote talk},
month = jun,
title = {FastFlow: high-level programming patterns with non-blocking lock-free run-time support},
url = {https://datacloud.di.unito.it/index.php/s/TJP46RfwX2NGPTS},
year = {2014},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2014_Dublin_Aldinucci.pdf}}

@misc{13:RAF:cloud,
address = {Madrid, Spain},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {Cloud and Science seminar at ``the Ramon Areces Foundation''},
keywords = {invited, misc},
month = mar,
note = {invited talk},
title = {Turning Big data into knowledge: systems biology droplets in the cloud},
url = {https://datacloud.di.unito.it/index.php/s/oBPqA5DeyCbj5do},
year = {2013},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2013_CWC_FRA.pdf}}

@misc{12:BIOIT:bigdata,
address = {Vienna, Austria},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {Bio-IT World Europe},
keywords = {invited, misc},
month = oct,
note = {invited talk},
title = {Turning Big data into knowledge: Techniques and Tools for Parallel Computing on Online Data Streams in Systems Biology and Epidemiology},
url = {https://datacloud.di.unito.it/index.php/s/BjLQyiDofH6gB3d},
year = {2012},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2012_BioIT_CWC.pdf}}

@misc{12:IEEE:denoising,
address = {Istanbul, Turkey},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {IEEE Intl. Conference on Image Processing Theory, Tools and Applications (IPTA)},
keywords = {misc},
month = oct,
title = {A Parallel Edge Preserving Algorithm for Salt and Pepper Image Denoising},
url = {https://datacloud.di.unito.it/index.php/s/JQe8RDp9jp4iymF},
year = {2012},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2012_IPTA_Aldinucci.pdf}}

@misc{12:UPMARC:fastflow,
address = {Uppsala, Sweden},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {UPMARC Workshop on Task-Based Parallel Programming},
keywords = {invited, misc},
month = sep,
note = {invited talk},
title = {FastFlow: high-level programming patterns with non-blocking lock-free run-time support},
url = {https://datacloud.di.unito.it/index.php/s/BWGrrsTWidMtcCn},
year = {2012},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2012_UPMARC.pdf}}

@misc{12:bigdata:ff,
address = {Rhodes, Greece},
author = {Marco Aldinucci},
date-added = {2021-01-01 17:49:10 +0100},
date-modified = {2021-02-01 11:21:34 +0100},
howpublished = {Workshop in big data management},
keywords = {keynote},
month = aug,
title = {Turning Big data into knowledge: Techniques and Tools for Parallel Computing on Online Data Streams in Systems Biology and Epidemiology},
url = {https://datacloud.di.unito.it/index.php/s/yg7i63MQdr7gZt7},
year = {2012},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/aldinuc/alpha-groupshare/Public/talks/2012_BDMC_Europar_Invited.pdf}}

@misc{12:HPCAC:denoising,
address = {Lugano, Switzerland},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {HPC Advisory Council Switzerland Conference},
keywords = {invited, misc},
month = mar,
note = {invited talk},
title = {Pattern-based Parallel Edge Preserving Algorithm for Salt-and-Pepper Image Denoising},
url = {https://datacloud.di.unito.it/index.php/s/BCm7w9S86yDNJb2},
year = {2012},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2012_HPCAC_UNITO-Aldinucci.pdf}}

M. Aldinucci, The camera that makes you beauty (with a novel real-time video-denoiser algorithm), TOSM expo Torino, Italy: , invited talk, nov, 2011.
[BibTeX]

@misc{11:TOSM:denoiser,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {TOSM expo},
keywords = {invited, misc},
month = nov,
note = {invited talk},
title = {The camera that makes you beauty (with a novel real-time video-denoiser algorithm)},
year = {2011},
Bdsk-Url-1 = {}}

M. Aldinucci, Performance and productivity in the multi-core era: challenges in software engineering and formal methods, IMT Lucca, Italy: , invited talk, nov, 2011.
[BibTeX] [Download PDF]

@misc{11:IMT:muticore,
address = {Lucca, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {IMT},
keywords = {invited, misc},
month = nov,
note = {invited talk},
title = {Performance and productivity in the multi-core era: challenges in software engineering and formal methods},
url = {https://datacloud.di.unito.it/index.php/s/Ppok8Cno7enc6QT},
year = {2011},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2011_IMT_ff.pdf}}

M. Aldinucci, FastFlow: Performance and Productivity in the Exascale Era, IBM Research, Exascale laboratory Dublin, Ireland: , invited talk, oct, 2011.
[BibTeX]

@misc{11:IBM:fastflow,
address = {Dublin, Ireland},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {IBM Research, Exascale laboratory},
keywords = {invited, misc},
month = oct,
note = {invited talk},
title = {FastFlow: Performance and Productivity in the Exascale Era},
year = {2011},
Bdsk-Url-1 = {}}

M. Aldinucci, FastFlow: Performance and Productivity in the Multicore Era, Formal Methods for Components and Objects (FMCO) Torino, Italy: , invited talk, oct, 2011.
[BibTeX] [Download PDF]

@misc{11:FMCO:fastflow,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {Formal Methods for Components and Objects (FMCO)},
keywords = {invited, misc},
month = oct,
note = {invited talk},
title = {FastFlow: Performance and Productivity in the Multicore Era},
url = {https://datacloud.di.unito.it/index.php/s/3opjTzm6XRcjYEs},
year = {2011},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2011_FMCO_ff.pdf}}

@misc{11:notteRicercatori:myMed,
address = {Torino, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {La notte dei ricercatori},
keywords = {invited, misc},
month = sep,
note = {invited talk},
title = {MyMed: Un social network geosensibile per reti fisse e mobili basato sul paradigma Peer-to-Peer},
url = {https://datacloud.di.unito.it/index.php/s/QjcSysM93X9DCDn},
year = {2011},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2011_09_Aldinucci-notte-Torino.pdf}}

@misc{11:HiBB:simulation,
address = {Bordeaux, France},
author = {Marco Aldinucci},
date-added = {2021-03-18 18:00:00 +0100},
date-modified = {2021-03-18 18:00:00 +0100},
howpublished = {Workshop on High Performance Bioinformatics and Biomedicine (HiBB)},
keywords = {misc},
month = aug,
title = {On Parallelizing On-Line Statistics for Stochastic Biological Simulations},
url = {https://datacloud.di.unito.it/index.php/s/3ijP5zx2ktT6Xab},
year = {2011},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2011_cwc_clustering_hibb_talk.pdf}}

M. Aldinucci, Accelerating code on multi-cores with FastFlow, Euro-Par 2011 Bordeaux, France: , aug, 2011.
[BibTeX] [Download PDF]

@misc{11:EUROPAR:fastflow,
address = {Bordeaux, France},
author = {Marco Aldinucci},
date-added = {2021-03-18 18:00:00 +0100},
date-modified = {2021-03-18 18:00:00 +0100},
howpublished = {Euro-Par 2011},
keywords = {misc},
month = aug,
title = {Accelerating code on multi-cores with FastFlow},
url = {https://datacloud.di.unito.it/index.php/s/Z4RtA9QoxCm829j},
year = {2011},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2011-sep-ff_acceleator_europar.pdf}}

@misc{11:COST:parallel,
address = {Limerick, Republic of Ireland},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {COST Action IC701 workshop},
keywords = {invited, misc},
month = jun,
note = {invited talk},
title = {High-level parallel programming: (few) ideas for challenges in formal methods},
url = {https://datacloud.di.unito.it/index.php/s/XfikGmd2e6pwfaM},
year = {2011},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2011_COST_IC701_ff.pdf}}

@misc{11:IEEE:simulator,
address = {Ayia Napa, Ciprus},
author = {Marco Aldinucci},
date-added = {2021-03-18 18:00:00 +0100},
date-modified = {2021-03-18 18:00:00 +0100},
howpublished = {IEEE Euromicro PDP 2011: Parallel Distributed and network-based Processing},
keywords = {misc},
month = feb,
title = {On Designing Multicore-Aware Simulators for Biological Systems},
url = {https://datacloud.di.unito.it/index.php/s/Anb4Gdj8p9yq4C2},
year = {2011},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2011_CWCsim_PDP.pdf}}

M. Aldinucci, FastFlow: a pattern-based programming framework for multicores, seminar 10191 Wadern, Germania: , invited talk, may, 2010.
[BibTeX] [Download PDF]

@misc{10:SCHOSS:fastflow,
address = {Wadern, Germania},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {seminar 10191},
keywords = {invited, misc},
month = may,
note = {invited talk},
title = {FastFlow: a pattern-based programming framework for multicores},
url = {https://datacloud.di.unito.it/index.php/s/fMpTjXat97kp2Hp},
year = {2010},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2010_FastFlow_PDP.pdf}}

M. Aldinucci, Efficient Smith-Waterman on multi-core with FastFlow, PDP 2010: Parallel Distributed and network-based Processing Pisa, Italy: , feb, 2010.
[BibTeX] [Download PDF]

@misc{10:PDP:fastflow,
address = {Pisa, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 18:00:00 +0100},
date-modified = {2021-03-18 18:00:00 +0100},
howpublished = {PDP 2010: Parallel Distributed and network-based Processing},
keywords = {misc},
month = feb,
title = {Efficient Smith-Waterman on multi-core with FastFlow},
url = {https://datacloud.di.unito.it/index.php/s/2RM8k6j3QGo5R7C},
year = {2010},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2010_FastFlow_PDP.pdf}}

M. Aldinucci, Efficient streaming applications on multi-core with FastFlow: the biosequence alignment test-bed, ParCo 2009 Lyon, France: , sep, 2009.
[BibTeX] [Download PDF]

@misc{09:PARCO:fastflow,
address = {Lyon, France},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {ParCo 2009},
keywords = {misc},
month = sep,
title = {Efficient streaming applications on multi-core with FastFlow: the biosequence alignment test-bed},
url = {https://datacloud.di.unito.it/index.php/s/Aa26ZyrfSB7ZWzj},
year = {2009},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2009_FastFlow_ParCo.pdf}}

M. Aldinucci, STKM on SCA: A unified framework with components, workflows and algorithmic skeletons, EuroPar 2009 Delft, The Netherlands: , sep, 2009.
[BibTeX] [Download PDF]

@misc{09:EUROPAR:stkm,
address = {Delft, The Netherlands},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {EuroPar 2009},
keywords = {misc},
month = sep,
title = {STKM on SCA: A unified framework with components, workflows and algorithmic skeletons},
url = {https://datacloud.di.unito.it/index.php/s/KDyBfqmt9HXXwKT},
year = {2009},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2009_STKM_EuroPar.pdf}}

@misc{09:PDP:hierarchical,
address = {Weimar, Germany},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {Euromicro PDP 2009: Parallel Distributed and network-based Processing},
keywords = {misc},
month = feb,
title = {Towards Hierarchical Management of Autonomic Components: a Case Study},
url = {https://datacloud.di.unito.it/index.php/s/KTn3dGwQwbe3bMc},
year = {2009},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2009_hierarchic_PDP.pdf}}

M. Aldinucci, Towards a Formal Semantics for Autonomic Components, CoreGRID Symposium Las Palmas de Gran Canaria, Canary Island, Spain: , aug, 2008.
[BibTeX]

@misc{08:COREGRID:semantics,
address = {Las Palmas de Gran Canaria, Canary Island, Spain},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {CoreGRID Symposium},
keywords = {misc},
month = aug,
title = {Towards a Formal Semantics for Autonomic Components},
year = {2008},
Bdsk-Url-1 = {}}

M. Aldinucci, Autonomic components in GCM, CoreGRID Scientific Advisory Board Amsterdam, The Netherland: , invited talk, may, 2008.
[BibTeX] [Download PDF]

@misc{08:COREGRID:autonomic,
address = {Amsterdam, The Netherland},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {CoreGRID Scientific Advisory Board},
keywords = {invited, misc},
month = may,
note = {invited talk},
title = {Autonomic components in GCM},
url = {https://datacloud.di.unito.it/index.php/s/22S3LwsTjbkG3w3},
year = {2008},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2008_CGSymph_compsem.pdf}}

M. Aldinucci, VirtuaLinux: Una soluzione open source per il clustering HPC, Net & System Security Pisa, Italia: , invited talk in italian, nov, 2007.
[BibTeX] [Download PDF]

@misc{07:NSS:virtualLinux,
address = {Pisa, Italia},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {Net & System Security},
keywords = {invited, misc},
month = nov,
note = {invited talk in italian},
title = {VirtuaLinux: Una soluzione open source per il clustering HPC},
url = {https://datacloud.di.unito.it/index.php/s/4SWFntywGgDtoxx},
year = {2007},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2007_Virtualinux_NSS.pdf}}

@misc{07:COREGRID:skeletons,
address = {Heraklion, Crete, Greece},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {CoreGRID Workshop on Grid Programming Model, Grid and P2P Systems Architecture, Grid Systems, Tools and Environments},
keywords = {misc},
month = jun,
title = {Behavioural skeletons for component autonomic management on grids},
url = {https://datacloud.di.unito.it/index.php/s/ZpidDEMJo8Pc4nB},
year = {2007},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2007_BeSke_Crete.pdf}}

@misc{06:LACS:adptation,
address = {San Cristobal de la Laguna, Tenerife, Canarian Islands, Spain},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {Last Advances in Computer Science},
keywords = {invited, misc},
month = nov,
note = {invited talk},
title = {Taming the grid through dynamic adaptation: results and open problems},
url = {https://datacloud.di.unito.it/index.php/s/2i8tDjMYEYnZeFo},
year = {2006},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2006_LaLaguna_UAI.pdf}}

M. Aldinucci, Fault-tolerant data sharing for high-level grid programming: a hierarchical storage architecture, CoreGRID Integration Workshop Krakow, Poland: , oct, 2006.
[BibTeX] [Download PDF]

@misc{06:COREGRID:storage,
address = {Krakow, Poland},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {CoreGRID Integration Workshop},
keywords = {misc},
month = oct,
title = {Fault-tolerant data sharing for high-level grid programming: a hierarchical storage architecture},
url = {https://datacloud.di.unito.it/index.php/s/iibrkEtMREHiexA},
year = {2006},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2006_IW_assistjuxmem.pdf}}

M. Aldinucci, Autonomic QoS in ASSIST Grid-aware components, IEEE Euromicro PDP 2006: Parallel Distributed and network-based Processing Montbéliard, France: , feb, 2006.
[BibTeX] [Download PDF]

@misc{06:IEEE:qos,
address = {Montb{\'e}liard, France},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {IEEE Euromicro PDP 2006: Parallel Distributed and network-based Processing},
keywords = {misc},
month = feb,
title = {Autonomic QoS in ASSIST Grid-aware components},
url = {https://datacloud.di.unito.it/index.php/s/bcCddKkiF9KsDWM},
year = {2006},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2006_assist_QoS_pdp_talk.pdf}}

M. Aldinucci, Building Interoperable Grid-aware ASSIST Applications via Web Services, ParCo 2005 Malaga, Spain: , sep, 2005.
[BibTeX] [Download PDF]

@misc{05:PARCO:assist,
address = {Malaga, Spain},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {ParCo 2005},
keywords = {misc},
month = sep,
title = {Building Interoperable Grid-aware ASSIST Applications via Web Services},
url = {https://datacloud.di.unito.it/index.php/s/6rbd7RWE6fbMbsi},
year = {2005},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2005_WS_parco_talk.pdf}}

M. Aldinucci, Towards a distributed scalable data service for the Grid, ParCo 2005 Malaga, Spain: , sep, 2005.
[BibTeX] [Download PDF]

@misc{05:PARCO:dataService,
address = {Malaga, Spain},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {ParCo 2005},
keywords = {misc},
month = sep,
title = {Towards a distributed scalable data service for the Grid},
url = {https://datacloud.di.unito.it/index.php/s/zQMGHBAWRLdKsbH},
year = {2005},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2005_ADHOC_parco_talk.pdf}}

M. Aldinucci, Dynamic reconfiguration of grid-aware applications in ASSIST, Euro-Par 2005 Lisbon, Portugal: , sep, 2005.
[BibTeX] [Download PDF]

@misc{05:EUROPAR:assist,
address = {Lisbon, Portugal},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {Euro-Par 2005},
keywords = {misc},
month = sep,
title = {Dynamic reconfiguration of grid-aware applications in ASSIST},
url = {https://datacloud.di.unito.it/index.php/s/nPESNMGWirAYEFB},
year = {2005},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2005_assist_dyn_europar_talk.pdf}}

M. Aldinucci, Rendering Grid Heterogeneity Harmless, Seminar 04451 – Future Generation Grids Dagstuhl, Germany: , invited talk, nov, 2004.
[BibTeX] [Download PDF]

@misc{04:FGG:heterogeneity,
address = {Dagstuhl, Germany},
author = {Marco Aldinucci},
date-added = {2021-03-18 17:00:00 +0100},
date-modified = {2021-03-18 17:00:00 +0100},
howpublished = {Seminar 04451 -- Future Generation Grids},
keywords = {invited, misc},
month = nov,
note = {invited talk},
title = {Rendering Grid Heterogeneity Harmless},
url = {https://datacloud.di.unito.it/index.php/s/ofZiZrd8YpWgqZD},
year = {2004},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2004_dagstuhl_talk.pdf}}

M. Aldinucci, Accelerating Apache farms through ad-HOC distributed scalable object repository, Euro-Par 2004 Pisa, Italy: , sep, 2004.
[BibTeX] [Download PDF]

@misc{04:EUROPAR:apache,
address = {Pisa, Italy},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {Euro-Par 2004},
keywords = {misc},
month = sep,
title = {Accelerating Apache farms through ad-HOC distributed scalable object repository},
url = {https://datacloud.di.unito.it/index.php/s/zLXTRRQb2rJNyDy},
year = {2004},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2004_adhoc_europar_talk.pdf}}

M. Aldinucci, Optimization techniques for implementing parallel skeletons in Grid, CMPP 2004 (in conjunction with MPC 04) Stirling, Scotland: , jul, 2004.
[BibTeX] [Download PDF]

@misc{04:CMPP:skeletons,
address = {Stirling, Scotland},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {CMPP 2004 (in conjunction with MPC 04)},
keywords = {misc},
month = jul,
title = {Optimization techniques for implementing parallel skeletons in Grid},
url = {https://datacloud.di.unito.it/index.php/s/MozKWbWPCxko2CX},
year = {2004},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2004_lithium_cmpp_talk.pdf}}

M. Aldinucci, An operational semantics for skeletons, ParCo 2003 Dresden, Germany: , sep, 2003.
[BibTeX] [Download PDF]

@misc{03:PARCO:skeletons,
address = {Dresden, Germany},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {ParCo 2003},
keywords = {misc},
month = sep,
title = {An operational semantics for skeletons},
url = {https://datacloud.di.unito.it/index.php/s/xDnFpT6RM9LHAEd},
year = {2003},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2003_semantics_parco_talk.pdf}}

M. Aldinucci, A framework for experimenting with structured parallel programming environment design, ParCo 2003 Dresden, Germany: , sep, 2003.
[BibTeX] [Download PDF]

@misc{03:PARCO:framework,
address = {Dresden, Germany},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {ParCo 2003},
keywords = {misc},
month = sep,
title = {A framework for experimenting with structured parallel programming environment design},
url = {https://datacloud.di.unito.it/index.php/s/FkGk3giN8MiGMwo},
year = {2003},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2003_ASSIST_parco_talk.pdf}}

@misc{03:EUROPAR:assist,
address = {Austria},
author = {Marco Aldinucci},
date-added = {2021-03-18 21:00:00 +0100},
date-modified = {2021-03-18 21:00:00 +0100},
howpublished = {9th Intl Euro-Par 2003: Parallel and Distributed Computing},
keywords = {misc},
month = aug,
title = {ASSIST demo: a high level, high performance, portable, structured parallel programming environment at work},
url = {https://datacloud.di.unito.it/index.php/s/fmEcpJDGD4iitia},
year = {2003},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2003_assist_poster_europar_a4.pdf}}

M. Aldinucci, eskimo: experimenting skeletons on the shared address model, HLPP 2003 Paris, France: , jun, 2003.
[BibTeX] [Download PDF]

@misc{03:HLPP:eskimo,
address = {Paris, France},
author = {Marco Aldinucci},
date-added = {2021-03-18 22:00:00 +0100},
date-modified = {2021-03-18 22:00:00 +0100},
howpublished = {HLPP 2003},
keywords = {misc},
month = jun,
title = {eskimo: experimenting skeletons on the shared address model},
url = {https://datacloud.di.unito.it/index.php/s/noo2N7HBKWm2nMH},
year = {2003},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2003_eskimo_hlpp_talk.pdf}}

M. Aldinucci, The META transformation tool for skeleton-based language, CMPP 2000 Ponte de Lima, Portugal: , jul, 2000.
[BibTeX] [Download PDF]

@misc{00:CMPP:META,
address = {Ponte de Lima, Portugal},
author = {Marco Aldinucci},
date-added = {2021-03-18 22:00:00 +0100},
date-modified = {2021-03-18 22:00:00 +0100},
howpublished = {CMPP 2000},
keywords = {misc},
month = jul,
title = {The META transformation tool for skeleton-based language},
url = {https://datacloud.di.unito.it/index.php/s/e9L5fWDo9q5fmw4},
year = {2000},
Bdsk-Url-1 = {https://datacloud.di.unito.it/index.php/apps/sharingpath/mittone/Shared/alpha-groupshare/Public/talks/2000_meta_cmpp_talk.pdf}}