摘要 : This paper shows how the Performance of singular value decomposition (SVD) is enhanced through the exploitation of ILP, TLP, and DLP on Intel multi-core processors using superscalar execution, multi-threading computation, and Stre... 展开 This paper shows how the Performance of singular value decomposition (SVD) is enhanced through the exploitation of ILP, TLP, and DLP on Intel multi-core processors using superscalar execution, multi-threading computation, and Streaming SIMD extensions, respectively. To facilitate the exploitation of TLP on multiple execution cores, the well-known cyclic one-sided Jacobi algorithm is restructured to work in parallel. On two dual-core Intel Xeon processors with hyper-threading technology running at 3.0 GHz, our results show that the multi-threaded implementation of one-sided Jacobi SVD gives about four times faster than the Single-threaded superscalar implementation. Furthermore, the multi-threaded SIMD implementation speeds up the execution of single-threaded one-sided Jacobi by a factor of 10, which is close to the ideal speedup. On a reasonable large matrix size fitted in the L2 cache, our results show a Performance of 11 GFLOPS (double-precision) is achieved on the target System through the exploitation of ILP, TLP, and DLP as well as memory hierarchy. 收起