Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
In-Situ Techniques on GPU-Accelerated Data-Intensive Applications
Max Planck Computing and Data Facility, Max Planck Computing and Data Facility.
Technical University of Munich, Technical University of Munich.
KTH, Skolan för teknikvetenskap (SCI), Teknisk mekanik, Strömningsmekanik och Teknisk Akustik.ORCID-id: 0000-0001-5204-8549
CINECA, Cineca.
Vise andre og tillknytning
2023 (engelsk)Inngår i: Proceedings 2023 IEEE 19th International Conference on e-Science, e-Science 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The computational power of High-Performance Computing (HPC) systems is constantly increasing, however, their input/output (IO) performance grows relatively slowly, and their storage capacity is also limited. This unbalance presents significant challenges for applications such as Molecular Dynamics (MD) and Computational Fluid Dynamics (CFD), which generate massive amounts of data for further visualization or analysis. At the same time, checkpointing is crucial for long runs on HPC clusters, due to limited walltimes and/or failures of system components, and typically requires the storage of large amount of data. Thus, restricted IO performance and storage capacity can lead to bottlenecks for the performance of full application workflows (as compared to computational kernels without IO). In-situ techniques, where data is further processed while still in memory rather to write it out over the I/O subsystem, can help to tackle these problems. In contrast to traditional post-processing methods, in-situ techniques can reduce or avoid the need to write or read data via the IO subsystem. They offer a promising approach for applications aiming to leverage the full power of large scale HPC systems. In-situ techniques can also be applied to hybrid computational nodes on HPC systems consisting of graphics processing units (GPUs) and central processing units (CPUs). On one node, the GPUs would have significant performance advantages over the CPUs. Therefore, current approaches for GPU-accelerated applications often focus on maximizing GPU usage, leaving CPUs underutilized. In-situ tasks using CPUs to perform data analysis or preprocess data concurrently to the running simulation, offer a possibility to improve this underutilization.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE) , 2023.
Emneord [en]
CPU, GPU, HPC, in-situ
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-338984DOI: 10.1109/e-Science58273.2023.10254865Scopus ID: 2-s2.0-85174292669OAI: oai:DiVA.org:kth-338984DiVA, id: diva2:1808750
Konferanse
19th IEEE International Conference on e-Science, e-Science 2023, Limassol, Cyprus, Oct 9 2023 - Oct 14 2023
Merknad

Part of ISBN 9798350322231

QC 20231101

Tilgjengelig fra: 2023-11-01 Laget: 2023-11-01 Sist oppdatert: 2023-12-04bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstScopus

Person

Perez, AdalbertoJansson, NiclasMarkidis, StefanoSchlatter, Philipp

Søk i DiVA

Av forfatter/redaktør
Perez, AdalbertoJansson, NiclasMarkidis, StefanoSchlatter, Philipp
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 132 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf