MPI collective algorithm selection and quadtree encoding

Authors:
Jelena Pješivac-Grbović;George Bosilca;Graham E. Fagg;Thara Angskun;Jack J. Dongarra
Affiliations:
Innovative Computing Laboratory, University of Tennessee Computer Science Department, Knoxville, TN 37996-3450, USA;Innovative Computing Laboratory, University of Tennessee Computer Science Department, Knoxville, TN 37996-3450, USA;Innovative Computing Laboratory, University of Tennessee Computer Science Department, Knoxville, TN 37996-3450, USA;Innovative Computing Laboratory, University of Tennessee Computer Science Department, Knoxville, TN 37996-3450, USA;Innovative Computing Laboratory, University of Tennessee Computer Science Department, Knoxville, TN 37996-3450, USA
Venue:
Parallel Computing
Year:
2007

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Abstract

We explore the applicability of the quadtree encoding method to the run-time MPI collective algorithm selection problem. Measured algorithm performance data was used to construct quadtrees with different properties. The quality and performance of generated decision functions and in-memory decision systems were evaluated. Experimental data shows that in some cases, a decision function based on a quadtree structure with a mean depth of three, incurs on average as little as a 5% performance penalty. In all cases, experimental data can be fully represented with a quadtree containing a maximum of six levels. Our results indicate that quadtrees may be a feasible choice for both processing of the performance data and automatic decision function generation.