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Improving the Performance of Parallel SpMV Operations on NUMA Systems with Adaptive Load Balancing

  • For a parallel Sparse Matrix Vector Multiply (SpMV) on a multiprocessor, rather simple and efficient work distributions often produce good results. In cases where this is not true, adaptive load balancing can improve the balance and performance. This paper introduces a low overhead framework for adaptive load balancing of parallel SpMV operations. It uses statistical filters to gather relevant runtime performance data and detects an imbalance situation. Three different algorithms were compared that adaptively balance the load with high quality and low overhead. Results show that for sparse matrices, where the adaptive load balancing was enabled, an average speedup of 1.15 (regarding the total execution time) could be achieved with our best algorithm over 4 different matrix formats and two different NUMA systems.

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Document Type:Part of a Book
Author:Christian Neugebauer, Rudolf Berrendorf, Florian Mannuss
Parent Title (English):Bassini, Danelutto et al. (Eds.): Parallel computing is everywhere. Advances in Parallel Computing, vol. 32
First Page:445
Last Page:454
Publisher:IOS Press
Place of publication:Amsterdam
Publication year:2018
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2018/05/04