Talks | Parallel Computing

@misc{24:santimaria:bighpc,

title = {Releasing the CAPIO middleware from MPI derived constraints},

author = {Marco Edoardo Santimaria and Iacopo Colonnelli and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/zrJGD4i36fWdp5g},

year  = {2024},

date = {2024-09-01},

address = {Pisa, Italy},

abstract = {CAPIO is a middleware that transparently injects streaming capabilities into file-based workflows. However, its implementation is limited to HPC environments based on the MPI framework, significantly limiting its applications. This paper will illustrate a proposed architecture and some preliminary results aimed at investigating the usage of a distributed files system as a communication media for the CAPIO middleware, with the ultimate goal of supporting both CLOUD-based and HPC-based workflows.},

keywords = {across, admire, capio, capiocl, eupex, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{24:santimaria:itadata:shpcpee,

title = {CAPIO: Cross Application Programamble IO},

author = {Marco Edoardo Santimaria and Iacopo Colonnelli and Massimo Torquati and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/rg6LWwrZXi6tTXm},

year  = {2024},

date = {2024-09-01},

address = {Pisa, Italy},

abstract = {With the increasing amount of digital data available for analysis and simulation, the class of I/O-intensive HPC workflows is fated to expand, further exacerbating quickly the performance gap between computing, memory, and storage technologies. CAPIO (Cross-Application Programmable I/O), is a middleware capable of injecting I/O streaming capabilities into file-based workflows, improving the computation-I/O overlap without the need to change the application code. In this presentation, we will introduce the CAPIO-CL language with its semantics, as well as the implementation of the CAPIO-CL language through the CAPIO middleware. We will also provide some case studies of how CAPIO has been employed to improve workflow execution time as well as some future directions.},

keywords = {across, admire, capio, capiocl, eupex, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{24:santimaria:hlpp:capiocl,

title = {CAPIO-CL: Cross Application Programmable IO - Coordination Language},

author = {Marco Edoardo Santimaria},

url = {https://datacloud.di.unito.it/index.php/s/zsKY3PWzX5NFCiX},

year  = {2024},

date = {2024-07-01},

address = {Pisa, Italy},

abstract = {The performance bottleneck in file-based workflows remains a pressing issue in the realm of I/O-based workflows. To address this challenge, a novel annotation language has been developed. CAPIO-CL is positioned as an innovative I/O coordination language, enabling users to annotate data dependencies within file-based workflows with synchronization semantics pertinent to the involved files and directories. Through the information provided by the language, optimization opportunities arise in streaming and preemptive data movement. This paper serves to illustrate the semantics and syntax enabling CAPIO-CL to enhance the performance of in situ workflows without necessitating the rewriting or modification of the original workflow application steps. Finally, an analysis of CAPIO-CL is provided, taking into consideration both language expressiveness and application performance enhancement.},

keywords = {across, admire, capio, eupex, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:ADMIRE:UG-SHA,

title = {The Software Heritage Analytics Framework},

author = {Barbara Cantalupo},

url = {https://datacloud.di.unito.it/index.php/s/NbNDPaGZkDc3KyN},

year  = {2023},

date = {2023-12-01},

address = {Barcelona Supercomputing Centre, Barcelona, Spain},

abstract = {A framework for analytics on top of Software Heritage},

howpublished = {Admire User Meeting, 2023},

keywords = {admire},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:ADMIRE:UG-GN,

title = {GreatNector Modelling Tool},

author = {Simone Pernice},

url = {https://datacloud.di.unito.it/index.php/s/NbNDPaGZkDc3KyN},

year  = {2023},

date = {2023-12-01},

address = {Barcelona Supercomputing Centre, Barcelona, Spain},

abstract = {A tool for system modelling I/O queue behaviour},

howpublished = {Admire User Meeting, 2023},

keywords = {admire},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:gtc:fl,

title = {Experimenting with Systems for Decentralized Machine Learning},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/oyLt7xwkbKxz65c},

year  = {2023},

date = {2023-03-01},

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.},

howpublished = {NVidia GTC 2023},

keywords = {across, admire, epi, eumaster4hpc, eupex, eupilot, hpc4ai, space, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:CENTAI:hpc4ai,

title = {HPC4AI: The Research on AI beyond the public cloud},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/PZXjPm8sfKTmTGb},

year  = {2023},

date = {2023-03-01},

address = {Torino, Italy},

howpublished = {CENTAI kick-off meeting},

keywords = {across, admire, brainteaser, epi, eumaster4hpc, eupex, eupilot, hpc4ai, space, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:Dell:hpc4ai,

title = {From HPC4AI to ICSC living lab: Where systems are the research},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/M5QRJyDxyxokcfL},

year  = {2023},

date = {2023-02-01},

address = {Bologna, Italy},

howpublished = {Dell Advanced Computing Workshop 2023: HPC and Beyond},

keywords = {admire, eupex, eupilot, hpc4ai, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:olimpiadi:cs,

title = {Il calcolo parallelo: una storia di metodi e algoritmi raccontata dalle macchine},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/7ZdfLkn3NetzXCN},

year  = {2022},

date = {2022-09-01},

address = {Biella, Italy},

abstract = {Lectio Magistralis alle finali nazionali delle Olimpiadi di Informatica 2022},

howpublished = {Olimpiadi di Informatica},

note = {Invited talk},

keywords = {across, admire, eumaster4hpc, eupex, eupilot, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:soiel:ai,

title = {La convergenza HPC-cloud è l'anello mancante tra il calcolo scientifico e l'IA applicata},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/xCQSqJ8bCKCXMK9},

year  = {2022},

date = {2022-09-01},

address = {Virtual event},

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.},

howpublished = {Intelligenza Artificiale e Business Applications},

note = {Invited talk},

keywords = {across, admire, eumaster4hpc, eupex, eupilot, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:itwshpc,

title = {Hybrid workflows for heterogeneous distributed computing},

author = {Iacopo Colonnelli and Barbara Cantalupo and Doriana Medić and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/ienbcA2DJ26aioE},

year  = {2022},

date = {2022-09-01},

address = {Torino, Italy},

howpublished = {3rd Italian Workshop on HPC (ITWSHPC)},

keywords = {across, admire, eumaster4hpc, eupex, eupilot, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:nvidia_hpc_roundtable,

title = {CINI HPC-KTT: HPC Key Technologies and Tools National Lab},

author = {Iacopo Colonnelli and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/9EQniZ2dGzdJ26f},

year  = {2022},

date = {2022-09-01},

address = {Casalecchio di Reno, Italy},

howpublished = {NVIDIA HPC Roundtable},

note = {Invited talk},

keywords = {across, admire, eumaster4hpc, eupex, eupilot, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:cip:romania,

title = {EuroHPC and the Italian HPC ecosystem},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/5dFFoNsZzwTzQkn},

year  = {2022},

date = {2022-06-01},

address = {Bucharest, 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.},

howpublished = {Critical Infrastructure Protection Forum - EuroCC Romania},

note = {Invited talk},

keywords = {across, admire, eumaster4hpc, eupex, eupilot, icsc, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:eocoe:summit,

title = {The Italian HPC ecosystem and the next generation of EuroHPC CoE},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/AH5Ms3NekeoEooB},

year  = {2022},

date = {2022-06-01},

address = {Napoli, Italy},

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.},

howpublished = {EuroHPC EoCoE final summit},

note = {Invited talk},

keywords = {across, admire, eumaster4hpc, eupex, eupilot, icsc, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:p3s:capio,

title = {From small files to no files},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/KLDi87xQmX86iXg},

year  = {2022},

date = {2022-06-01},

address = {Paris, France},

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.},

howpublished = {6th Workshop on Performance and Scalability of Storage Systems},

note = {Invited talk},

keywords = {admire, eupex},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:hipeacvision:fl,

title = {Cognitive continuum: a game theoretical approach},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/453HWfmrQyo7j9E},

year  = {2022},

date = {2022-05-01},

address = {Brussels, Belgium},

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},

howpublished = {HiPEAC Vision meeting, Brussels, 16 May 2022},

keywords = {across, admire, brainteaser, eumaster4hpc, eupex, eupilot, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:garr,

title = {Da HPC4AI al living lab dello spoke FutureHPC del Centro Nazionale HPC},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/P3KSroSSmrRxZMc},

year  = {2022},

date = {2022-05-01},

address = {Palermo, Italy},

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.},

howpublished = {Condivisioni, Conferenza GARR 2022},

note = {Keynote talk},

keywords = {across, admire, eumaster4hpc, eupex, hpc4ai, icsc, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:eni:streamflow,

title = {The modernization of HPC applications for the cloud era},

author = {Marco Aldinucci},

year  = {2021},

date = {2021-09-01},

address = {Virtual event},

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.},

howpublished = {Fifth EAGE Workshop on High Performance Computing for Upstream},

note = {Keynote talk},

keywords = {across, admire, deephealth, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:hlpp:streamflow,

title = {From skeletons to workflows in the cloud-edge era},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/RyRPjNBse5PKnab},

year  = {2021},

date = {2021-07-01},

address = {Virtual event},

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.},

howpublished = {14th Intl. Symposium on High-Level Programming and Applications (HLPP)},

note = {Keynote talk},

keywords = {across, admire, deephealth, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:penta:covid,

title = {Reproducibility in the AI era},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/GLpf7kKSJRH733A},

year  = {2021},

date = {2021-07-01},

address = {Virtual event},

abstract = {TBD},

howpublished = {Penta Scientific Meeting},

keywords = {across, admire, deephealth},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:CINI_acm_CF_talk,

title = {The Italian research on HPC key technologies across EuroHPC},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/3ZYmDbEm84rbB9k},

year  = {2021},

date = {2021-05-01},

booktitle = {ACM Computing Frontiers},

publisher = {ACM},

address = {Virtual Conference, Italy},

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.},

keywords = {across, admire, eupex, eupilot, textarossa},

pubstate = {published},

tppubtype = {misc}

}