Computer Science – Performance
Scientific paper
2009-10-26
High Performance Computing in Science and Engineering, Garching/Munich 2009. Springer, (2010), 13-26
Computer Science
Performance
16 pages, 9 figures
Scientific paper
10.1007/978-3-642-13872-0_2
The increasing importance of multicore processors calls for a reevaluation of established numerical algorithms in view of their ability to profit from this new hardware concept. In order to optimize the existent algorithms, a detailed knowledge of the different performance-limiting factors is mandatory. In this contribution we investigate sparse matrix-vector multiplication, which is the dominant operation in many sparse eigenvalue solvers. Two conceptually different storage schemes and computational kernels have been conceived in the past to target cache-based and vector architectures, respectively. Starting from a series of microbenchmarks we apply the gained insight on optimized sparse MVM implementations, whose serial and OpenMP-parallel performance we review on state-of-the-art multicore systems.
Fehske Holger
Hager Georg
Schubert Gerald
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