Papers | Parallel Computing

@inproceedings{23:hipc:capio,

title = {CAPIO: a Middleware for Transparent I/O Streaming in Data-Intensive Workflows},

author = {Alberto Riccardo Martinelli and Massimo Torquati and Marco Aldinucci and Iacopo Colonnelli and Barbara Cantalupo},

url = {https://iris.unito.it/retrieve/27380f37-0978-409e-a9d8-2b5e95a4bb85/CAPIO-HiPC23-preprint.pdf},

doi = {10.1109/HiPC58850.2023.00031},

year  = {2023},

date = {2023-12-01},

booktitle = {2023 IEEE 30th International Conference on High Performance Computing, Data, and Analytics (HiPC)},

publisher = {IEEE},

address = {Goa, India},

abstract = {With the increasing amount of digital data available for analysis and simulation, the class of I/O-intensive HPC workflows is fated to quickly expand, further exacerbating the performance gap between computing, memory, and storage technologies. This paper introduces CAPIO (Cross-Application Programmable I/O), 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. The contribution is twofold: 1) at design time, a new I/O coordination language allows users to annotate workflow data dependencies with synchronization semantics; 2) at run time, a user-space middleware automatically and transparently to the user turns a workflow batch execution into a streaming execution according to the semantics expressed in the configuration file. CAPIO has been tested on synthetic benchmarks simulating typical workflow I/O patterns and two real-world workflows. Experiments show that CAPIO reduces the execution time by 10% to 66% for data-intensive workflows that use the file system as a communication medium.},

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

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:admire:cf,

title = {Adaptive multi-tier intelligent data manager for Exascale},

author = {Jesus Carretero and Javier Garcia-Blas and Marco Aldinucci and Jean Baptiste Besnard Besnard and Jean-Thomas Acquaviva and André Brinkmann and Marc-André Vef and Emmanuel Jeannot and Alberto Miranda and Ramon Nou and Morris Riedel and Massimo Torquati and Felix Wolf},

url = {https://dl.acm.org/doi/pdf/10.1145/3587135.3592174},

doi = {10.1145/3587135.3592174},

year  = {2023},

date = {2023-05-01},

booktitle = {20th ACM International Conference on Computing Frontiers (CF '23)},

publisher = {ACM},

address = {Bologna, Italy},

abstract = {The main objective of the ADMIRE project1 is the creation of an active I/O stack that dynamically adjusts computation and storage requirements through intelligent global coordination, the elasticity of computation and I/O, and the scheduling of storage resources along all levels of the storage hierarchy, while offering quality-of-service (QoS), energy efficiency, and resilience for accessing extremely large data sets in very heterogeneous computing and storage environments. We have developed a framework prototype that is able to dynamically adjust computation and storage requirements through intelligent global coordination, separated control, and data paths, the malleability of computation and I/O, the scheduling of storage resources along all levels of the storage hierarchy, and scalable monitoring techniques. The leading idea in ADMIRE is to co-design applications with ad-hoc storage systems that can be deployed with the application and adapt their computing and I/O behaviour on runtime, using malleability techniques, to increase the performance of applications and the throughput of the applications.},

keywords = {admire},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{GARCIA:PDP:23,

title = {A Latency, Throughput, and Programmability Perspective of GrPPI for Streaming on Multi-cores},

author = {Adriano Marques Garcia and Dalvan Griebler and Claudio Schepke and André Sacilotto Santos and José Daniel García and Javier Fernández Muñoz and Luiz Gustavo Fernandes},

url = {https://iris.unito.it/retrieve/9165d2ef-7140-4645-87cc-269050341c1d/PDP_2023_SPbench_with_GrPPI.pdf},

doi = {10.1109/PDP59025.2023.00033},

year  = {2023},

date = {2023-03-01},

booktitle = {31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},

pages = {164-168},

publisher = {IEEE},

address = {Naples, Italy},

series = {PDP'23},

abstract = {Several solutions aim to simplify the burdening task of parallel programming. The GrPPI library is one of them. It allows users to implement parallel code for multiple backends through a unified, abstract, and generic layer while promising minimal overhead on performance. An outspread evaluation of GrPPI regarding stream parallelism with representative metrics for this domain, such as throughput and latency, was not yet done. In this work, we evaluate GrPPI focused on stream processing. We evaluate performance, memory usage, and programming effort and compare them against handwritten parallel code. For this, we use the benchmarking framework SPBench to build custom GrPPI benchmarks. The basis of the benchmarks is real applications, such as Lane Detection, Bzip2, Face Recognizer, and Ferret. Experiments show that while performance is competitive with handwritten code in some cases, in other cases, the infeasibility of fine-tuning GrPPI is a crucial drawback. Despite this, programmability experiments estimate that GrPPI has the potential to reduce by about three times the development time of parallel applications.},

keywords = {admire},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Antelmi_ITADATA_2023,

title = {The SWH-Analytics Framework},

author = {Alessia Antelmi and Massimo Torquati and Daniele Gregori and Francesco Polzella and Gianmarco Spinatelli and Marco Aldinucci},

editor = {Nicola Bena and Beniamino Di Martino and Antonio Maratea and Alessandro Sperduti and Emanuel Di Nardo and Angelo Ciaramella and Raffaele Montella and Claudio A. Ardagna},

url = {https://ceur-ws.org/Vol-3606/paper76.pdf},

year  = {2023},

date = {2023-01-01},

booktitle = {Proceedings of the 2nd Italian Conference on Big Data and Data Science (ITADATA 2023), Naples, Italy, September 11-13, 2023},

volume = {3606},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

abstract = {The Software Heritage (SWH) dataset serves as a vast repository for open-source code, with the ambitious goal of preserving all publicly available open-source projects. Despite being designed to effectively archive project files, its size of nearly 1 petabyte presents challenges in efficiently supporting Big Data MapReduce or AI systems. To address this disparity and enable seamless custom analytics on the SWH dataset, we present the SWH-Analytics (SWHA) architecture. This development environment quickly and transparently runs custom analytic applications on open-source software data preserved over time by SWH.},

keywords = {admire, analytics, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-031-39698-4_46,

title = {Hercules: Scalable and Network Portable In-Memory Ad-Hoc File System for Data-Centric and High-Performance Applications},

author = {Javier Garcia-Blas and Genaro Sanchez-Gallegos and Cosmin Petre and Alberto Riccardo Martinelli and Marco Aldinucci and Jesus Carretero},

editor = {José Cano and Marios D. Dikaiakos and George A. Papadopoulos and Miquel Pericàs and Rizos Sakellariou},

isbn = {978-3-031-39698-4},

year  = {2023},

date = {2023-01-01},

booktitle = {Euro-Par 2023: Parallel Processing},

pages = {679–693},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {The growing demands for data processing by new data-intensive applications are putting pressure on the performance and capacity of HPC storage systems. The advancement in storage technologies, such as NVMe and persistent memory, are aimed at meeting these demands. However, relying solely on ultra-fast storage devices is not cost-effective, leading to the need for multi-tier storage hierarchies to move data based on its usage. To address this issue, ad-hoc file systems have been proposed as a solution. They utilise the available storage of compute nodes, such as memory and persistent storage, to create a temporary file system that adapts to the application behaviour in the HPC environment. This work presents the design, implementation, and evaluation of a distributed ad-hoc in-memory storage system (Hercules), highlighting the new communication model included in Hercules. This communication model takes advantage of the Unified Communication X framework (UCX). This solution leverages the capabilities of RDMA protocols, including Infiniband, Onmipath, shared memory, and zero-copy transfers. The preliminary evaluation results show excellent network utilisation compared with other existing technologies.},

keywords = {admire, HPC},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/ftfjp/AngeloBDF23,

title = {Gradual Guarantee for FJ with lambda-Expressions},

author = {Pedro Ângelo and Viviana Bono and Mariangiola Dezani-Ciancaglini and Mário Florido},

editor = {Aaron Tomb},

url = {https://doi.org/10.1145/3605156.3606453},

doi = {10.1145/3605156.3606453},

year  = {2023},

date = {2023-01-01},

booktitle = {Proceedings of the 25th ACM International Workshop on Formal Techniques for Java-like Programs, FTfJP 2023, Seattle, WA, USA, 18 July 2023},

pages = {32–38},

publisher = {ACM},

keywords = {admire, icsc},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{23:praise-fl:pdp,

title = {Pooling critical datasets with Federated Learning},

author = {Yasir Arfat and Gianluca Mittone and Iacopo Colonnelli and Fabrizio D'Ascenzo and Roberto Esposito and Marco Aldinucci},

url = {https://iris.unito.it/retrieve/491e22ec-3db5-4989-a063-085a199edd20/23_pdp_fl.pdf},

doi = {10.1109/PDP59025.2023.00057},

year  = {2023},

date = {2023-01-01},

booktitle = {31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023},

pages = {329–337},

publisher = {IEEE},

address = {Napoli, Italy},

abstract = {Federated Learning (FL) is becoming popular in different industrial sectors where data access is critical for security, privacy and the economic value of data itself. Unlike traditional machine learning, where all the data must be globally gathered for analysis, FL makes it possible to extract knowledge from data distributed across different organizations that can be coupled with different Machine Learning paradigms. In this work, we replicate, using Federated Learning, the analysis of a pooled dataset (with AdaBoost) that has been used to define the PRAISE score, which is today among the most accurate scores to evaluate the risk of a second acute myocardial infarction. We show that thanks to the extended-OpenFL framework, which implements AdaBoost.F, we can train a federated PRAISE model that exhibits comparable accuracy and recall as the centralised model. We achieved F1 and F2 scores which are consistently comparable to the PRAISE score study of a 16- parties federation but within an order of magnitude less time.},

keywords = {admire, confidential, hpc4ai},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{21:CINI_acm_CF,

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

author = {Marco Aldinucci and Giovanni Agosta and Antonio Andreini and Claudio A. Ardagna and Andrea Bartolini and Alessandro Cilardo and Biagio Cosenza and Marco Danelutto and Roberto Esposito and William Fornaciari and Roberto Giorgi and Davide Lengani and Raffaele Montella and Mauro Olivieri and Sergio Saponara and Daniele Simoni and Massimo Torquati},

url = {https://iris.unito.it/retrieve/handle/2318/1783118/744641/preprint.pdf},

doi = {10.1145/3457388.3458508},

year  = {2021},

date = {2021-05-01},

booktitle = {ACM Computing Frontiers},

pages = {279–286},

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 = {admire, eupex, eupilot, textarossa},

pubstate = {published},

tppubtype = {inproceedings}

}