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Program Optimization Strategies to Improve the Performance of SpMV-Operations

Authors:
Rudolf Berrendorf
Max Weierstall
Florian Mannuss

Keywords: Sparse Matrix Vector multiply (SpMV); Single Instruction Multiple Data (SIMD); OpenMP, unrolling; intrinsics.

Abstract:
The SpMV operation -- the multiplication of a sparse matrix with a dense vector -- is used in many simulations in natural and engineering sciences as a computational kernel. This kernel is quite performance critical as it is used, e.g.,~in a linear solver many times in a simulation run. Such performance critical kernels of a program may be optimized on certain levels, ranging from using a rather coarse grained and comfortable single compiler optimization switch down to utilizing architecural features by explicitly using special instructions on an assembler level. This paper discusses a selection of such program optimization techniques in this spectrum applied to the SpMV operation. The achievable performance gain as well as the additional programming effort are discussed. It is shown that low effort optimizations can improve the performance of the SpMV operation compared to a basic implementation. But further than that, more complex low level optimizations have a higher impact on the performance, although changing the original program and the readability / maintainability of a program significantly.

Pages: 34 to 40

Copyright: Copyright (c) IARIA, 2016

Publication date: March 20, 2016

Published in: conference

ISSN: 2308-3735

ISBN: 978-1-61208-461-9

Location: Rome, Italy

Dates: from March 20, 2016 to March 24, 2016