Performance study of matrix computations using multi-core programming tools
Pages 186 - 192
Abstract
Basic matrix computations such as vector and matrix addition, dot product, outer product, matrix transpose, matrix - vector and matrix multiplication are very challenging computational kernels arising in scientific computing. In this paper, we parallelize those basic matrix computations using the multi-core and parallel programming tools. Specifically, these tools are Pthreads, OpenMP, Intel Cilk++, Intel TBB, Intel ArBB, SMPSs, SWARM and FastFlow. The purpose of this paper is to present an unified quantitative and qualitative study of these tools for parallel matrix computations on multicore. Finally, based on the performance results with compilation optimization we conclude that the Intel ArBB and SWARM parallel programming tools are the most appropriate because these give good performance and simplicity of programming. In particular, we conclude that the Intel ArBB is a good choice for implementing intensive computations such as matrix product because it gives significant speedup results over the serial implementation. On the other hand, the SWARM tool gives good performance results for implementing matrix operations of medium size such as vector addition, matrix addition, outer product and matrix - vector product.
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Index Terms
- Performance study of matrix computations using multi-core programming tools
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Published: 16 September 2012
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