Data-driven execution of fast multipole methods

Hatem Ltaief, Rio Yokota

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Fast multipole methods (FMMs) have O (N) complexity, are compute bound, and require very little synchronization, which makes them a favorable algorithm on next-generation supercomputers. Their most common application is to accelerate N-body problems, but they can also be used to solve boundary integral equations. When the particle distribution is irregular and the tree structure is adaptive, load balancing becomes a non-trivial question. A common strategy for load balancing FMMs is to use the work load from the previous step as weights to statically repartition the next step. The authors discuss in the paper another approach based on data-driven execution to efficiently tackle this challenging load balancing problem. The core idea consists of breaking the most time-consuming stages of the FMMs into smaller tasks. The algorithm can then be represented as a directed acyclic graph where nodes represent tasks and edges represent dependencies among them. The execution of the algorithm is performed by asynchronously scheduling the tasks using the queueing and runtime for kernels runtime environment, in a way such that data dependencies are not violated for numerical correctness purposes. This asynchronous scheduling results in an out-of-order execution. The performance results of the data-driven FMM execution outperform the previous strategy and show linear speedup on a quad-socket quad-core Intel Xeon system.Copyright © 2013 John Wiley & Sons, Ltd. Copyright © 2013 John Wiley & Sons, Ltd.
Original languageEnglish (US)
Pages (from-to)1935-1946
Number of pages12
JournalConcurrency and Computation: Practice and Experience
Volume26
Issue number11
DOIs
StatePublished - Oct 8 2013

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science
  • Software
  • Computer Networks and Communications
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Data-driven execution of fast multipole methods'. Together they form a unique fingerprint.

Cite this