Adapting a message-driven parallel application to GPU-accelerated clusters

James C. Phillips, John E. Stone, Klaus Schulten

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Graphics processing units (GPUs) have become an attractive option for accelerating scientific computations as a result of advances in the performance and flexibility of GPU hardware, and due to the availability of GPU software development tools targeting general purpose and scientific computation. However, effective use of GPUs in clusters presents a number of application development and system integration challenges. We describe strategies for the decomposition and scheduling of computation among CPU cores and GPUs, and techniques for overlapping communication and CPU computation with GPU kernel execution. We report the adaptation of these techniques to NAMD, a widely-used parallel molecular dynamics simulation package, and present performance results for a 64-core 64-GPU cluster.

Original languageEnglish (US)
Title of host publication2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2008
DOIs
StatePublished - Dec 1 2008
Event2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2008 - Austin, TX, United States
Duration: Nov 15 2008Nov 21 2008

Publication series

Name2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2008

Other

Other2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2008
CountryUnited States
CityAustin, TX
Period11/15/0811/21/08

Fingerprint

Program processors
Graphics processing unit
Molecular dynamics
Software engineering
Scheduling
Availability
Decomposition
Hardware
Communication
Computer simulation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

Cite this

Phillips, J. C., Stone, J. E., & Schulten, K. (2008). Adapting a message-driven parallel application to GPU-accelerated clusters. In 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2008 [5214716] (2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2008). https://doi.org/10.1109/SC.2008.5214716

Adapting a message-driven parallel application to GPU-accelerated clusters. / Phillips, James C.; Stone, John E.; Schulten, Klaus.

2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2008. 2008. 5214716 (2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2008).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Phillips, JC, Stone, JE & Schulten, K 2008, Adapting a message-driven parallel application to GPU-accelerated clusters. in 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2008., 5214716, 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2008, 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2008, Austin, TX, United States, 11/15/08. https://doi.org/10.1109/SC.2008.5214716
Phillips JC, Stone JE, Schulten K. Adapting a message-driven parallel application to GPU-accelerated clusters. In 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2008. 2008. 5214716. (2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2008). https://doi.org/10.1109/SC.2008.5214716
Phillips, James C. ; Stone, John E. ; Schulten, Klaus. / Adapting a message-driven parallel application to GPU-accelerated clusters. 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2008. 2008. (2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2008).
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