Search Results - "HPC I/O"

I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey

Authors: Noah Lewis Jean Luca Bez Suren Byna

Source: ACM Computing Surveys, vol 57, iss 10

Subject Terms: FOS: Computer and information sciences, Networking and Information Technology R&D (NITRD) (rcdc), Data Science (rcdc), H.3.2, H.3.4, I.2.11, I/O access pattern, Bioengineering (rcdc), 4606 Distributed Computing and Systems Software (for-2020), 08 Information and Computing Sciences (for), 7 Affordable and Clean Energy (sdg), HPC I/O, Machine Learning (cs.LG), 46 Information and Computing Sciences (for-2020), storage, Machine Learning, machine learning, Artificial Intelligence (cs.AI), Artificial Intelligence, Information Systems (science-metrix), 46 Information and computing sciences (for-2020), Distributed, Parallel, and Cluster Computing, Distributed, Parallel, and Cluster Computing (cs.DC), Machine Learning and Artificial Intelligence (rcdc)

File Description: application/pdf

Access URL: http://arxiv.org/abs/2404.10386
https://escholarship.org/content/qt8tc828xw/qt8tc828xw.pdf
https://escholarship.org/uc/item/8tc828xw

I/O Access Patterns in HPC Applications: A 360-Degree Survey

Authors: Bez, Jean Luca Byna, Suren Ibrahim, Shadi

Source: ACM Computing Surveys. 56(2)

Subject Terms: 46 Information and Computing Sciences (for-2020), 4612 Software Engineering (for-2020), I/O access pattern, HPC I/O, storage, I/O characterization, 08 Information and Computing Sciences (for), Information Systems (science-metrix), 46 Information and computing sciences (for-2020)

File Description: application/pdf

Access URL: https://escholarship.org/uc/item/198194vd
https://escholarship.org/content/qt198194vd/qt198194vd.pdf

Malleable I/O Applications Modeling with ElastiSim

Authors: Almaaitah, Njoud orcid:0000-0003-3702- David, E. Singh et al.

Subject Terms: HPC I/O, application modeling, malleability, I/O characterization, performance model

Relation: https://zenodo.org/records/12799776; oai:zenodo.org:12799776; https://doi.org/10.5281/zenodo.12799776

Availability: https://doi.org/10.5281/zenodo.12799776
https://zenodo.org/records/12799776

Understanding I/O Behavior in Scientific and Data-Intensive Computing (Dagstuhl Seminar 21332)

Authors: Carns, Philip Kunkel, Julian Mohror, Kathryn et al.

Contributors: Carns, Philip Kunkel, Julian Mohror, Kathryn et al.

Subject Terms: I/O performance measurement, Understanding user I/O patterns, HPC I/O, I/O characterization

File Description: application/pdf

Relation: Is Part Of Dagstuhl Reports, Volume 11, Issue 7 (2021); https://drops.dagstuhl.de/entities/document/10.4230/DagRep.11.7.16

Availability: https://doi.org/10.4230/DagRep.11.7.16
https://nbn-resolving.org/urn:nbn:de:0030-drops-155891
https://drops.dagstuhl.de/entities/document/10.4230/DagRep.11.7.16

Exploring transfer learning for predicting I/O time across systems

Authors: Voß, Adrian Müller, Matthias S. Kunkel, Julian et al.

Contributors: Voß, Adrian Müller, Matthias S. Kunkel, Julian et al.

Source: Aachen : RWTH Aachen University 1 Online-Ressource: Illustrationen (2024). doi:10.18154/RWTH-2024-09250 = Masterarbeit, RWTH Aachen University, 2024

Subject Terms: machine learning, I/O, HPC, deep learning, transfer learning, HPC , I/O , transfer learning , machine learning , deep learning , explainable AI, explainable AI

Access URL: https://publications.rwth-aachen.de/record/994335

Transfer learning workflow for I/O bandwidth prediction

Authors: Povaliaiev, Dmytro Müller, Matthias S. Kunkel, Julian et al.

Contributors: Povaliaiev, Dmytro Müller, Matthias S. Kunkel, Julian et al.

Source: Aachen : RWTH Aachen University 1 Online-Ressource : Illustrationen, Diagramme (2023). doi:10.18154/RWTH-2023-05063 = Masterarbeit, RWTH Aachen University, 2023

Subject Terms: machine learning, I/O, I/O bandwidth prediction, HPC, high-performance computing, interpretable machine learning, transfer learning, HPC I/O, explainable AI

Access URL: https://publications.rwth-aachen.de/record/958007

ADDING PERSISTENCE TO MAIN MEMORY PROGRAMMING

Authors: Fernando, Waduge Pradeep Rangana Gavrilovska, Ada Ramachandran, UmaKishore et al.

Contributors: Fernando, Waduge Pradeep Rangana Gavrilovska, Ada Ramachandran, UmaKishore et al.

Subject Terms: Persistent memory (PMEM), PMEM accelerated HPC I/O, Crash-consistent PMEM programming, Fault-tolerant PMEM programming

File Description: application/pdf

Relation: http://hdl.handle.net/1853/64530

Availability: http://hdl.handle.net/1853/64530

Understanding I/O Behavior in Scientific and Data-Intensive Computing (Dagstuhl Seminar 21332)

Authors: Philip Carns and Julian Kunkel and Kathryn Mohror and Martin Schulz Carns, Philip Kunkel, Julian et al.

Index Terms: I/O performance measurement, Understanding user I/O patterns, HPC I/O, I/O characterization, Data processing Computer science, InProceedings, publishedVersion

PIConGPU Particle-in-Cell Simulations of Transient High Energy Density Plasmas

Authors: (0000-0003-1943-7141) Huebl, A. Kluge, T. Garten, M. et al.

Source: EUCALL Annual Meeting 2017, 07.-09.06.2017, Grenoble, Frankreich

Index Terms: PIConGPU EUCALL non-LTE HPC I/O transient plasma processes, info:eu-repo/semantics/publishedVersion, doc-type:lecture, Text

URL: https://www.hzdr.de/publications/Publ-25635-1
info:eu-repo/semantics/altIdentifier/purl/https://www.hzdr.de/publications/Publ-25635-1