Parallelization of MD algorithms and load balancing

Alexander Heinecke, Wolfgang Eckhardt, Martin Horsch, Hans Joachim Bungartz

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

MD simulation in process engineering features enormous computational demands, and therefore requires efficient parallelization techniques. This chapter describes ls1 mardyn ’s parallelization approach for shared-memory and distributed-memory architectures. This is done by first defining today’s computing architectures and their governing design principles: Heterogeneity, massive amounts of cores and data parallelism. Based on this, we are then able to reengineer ls1 mardyn in such a way that it can optimally leverage important hardware features, and describe our parallelization approach for shared- and distributed-memory systems at the example of the Intel Xeon processor and the Intel Xeon Phi coprocessor, respectively. We close this section by describing load-balancing techniques in case of a distributed-memory parallelization and heterogeneous particle distributions in the computational domain.

Original languageEnglish
Title of host publicationSpringerBriefs in Computer Science
PublisherSpringer
Pages31-44
Number of pages14
Edition9783319171470
DOIs
StatePublished - 2015

Publication series

NameSpringerBriefs in Computer Science
Number9783319171470
Volume0
ISSN (Print)2191-5768
ISSN (Electronic)2191-5776

Keywords

  • Distributed-memory parallelization
  • KD-trees
  • Load-balancing
  • MPI
  • Molecular dynamics simulation
  • OpenMP
  • Shared-memory parallelization
  • Spatial decomposition

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