Direct self-consistent field computations on GPU clusters

Guochun Shi, Volodymyr Kindratenko, Ivan Ufimtsev, Todd Martinez

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

Abstract

We present an implementation of one of the direct self-consistent-field (DSCF) calculation techniques, the restricted Hartree-Fock method, on a high-performance computing cluster outfitted with graphics processing units (GPUs) and demonstrate its effectiveness and scalability up to 128 cluster nodes on molecules of as many as 1,732 atoms. We discuss the overall parallel application architecture that relies on message passing interface for distributing workload among GPU cluster nodes and POSIX threads to manage the use of GPUs internal to each node. This approach of combining coarse and fine-grain parallelism on a distributed memory system allows to perform DSCF calculations on molecules that up until now have been unattainable due to the excessive computational requirements.

Original languageEnglish (US)
Title of host publicationProceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2010
DOIs
StatePublished - Jul 1 2010
Event24th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2010 - Atlanta, GA, United States
Duration: Apr 19 2010Apr 23 2010

Publication series

NameProceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2010

Other

Other24th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2010
CountryUnited States
CityAtlanta, GA
Period4/19/104/23/10

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Keywords

  • GPU
  • Restricted Hartree-Fock

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Software
  • Theoretical Computer Science

Cite this

Shi, G., Kindratenko, V., Ufimtsev, I., & Martinez, T. (2010). Direct self-consistent field computations on GPU clusters. In Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2010 [5470478] (Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2010). https://doi.org/10.1109/IPDPS.2010.5470478