MPI collective algorithm selection and quadtree encoding

Authors:
Jelena Pješivac–Grbović;Graham E. Fagg;Thara Angskun;George Bosilca;Jack J. Dongarra
Affiliations:
Innovative Computing Laboratory, University of Tennessee Computer Science Department, Knoxville, TN;Innovative Computing Laboratory, University of Tennessee Computer Science Department, Knoxville, TN;Innovative Computing Laboratory, University of Tennessee Computer Science Department, Knoxville, TN;Innovative Computing Laboratory, University of Tennessee Computer Science Department, Knoxville, TN;Innovative Computing Laboratory, University of Tennessee Computer Science Department, Knoxville, TN
Venue:
EuroPVM/MPI'06 Proceedings of the 13th European PVM/MPI User's Group conference on Recent advances in parallel virtual machine and message passing interface
Year:
2006

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Abstract

Selecting the close-to-optimal collective algorithm based on the parameters of the collective call at run time is an important step in achieving good performance of MPI applications. In this paper, we focus on MPI collective algorithm selection process and explore the applicability of the quadtree encoding method to this problem. We construct quadtrees with different properties from the measured algorithm performance data and analyze the quality and performance of decision functions generated from these trees. The experimental data shows that in some cases, the decision function based on a quadtree structure with a mean depth of 3 can incur as little as a 5% performance penalty on average. The exact, experimentally measured, decision function for all tested collectives could be fully represented using quadtrees with a maximum of 6 levels. These results indicate that quadtrees may be a feasible choice for both processing of the performance data and automatic decision function generation.