SpMV Profiling and Optimization Analysis
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2015, Vol 17, Issue 4
Abstract
Abstract: Sparse matrix-vector multiplication is an important operation when it comes to sparse matrixcomputations. Very large and sparse matrices are used in many engineering and scientific operations. Hencethe matrix needs to be partitioned properly. Even though the matrix is partitioned and stored appropriatelythere still exists a possibility, the performance achieved is not significant. Thus, the need to address these issues.System proposes an integrated analytical and profile based performance modelling that accurately measures thekernel execution time of various SpMV CUDA kernels for a given target sparse-matrix. Based on this thedesigned optimal solution auto-selection algorithm automatically reports the SpMV optimal solution for a targetsparse-matrix. The system is evaluated on NVIDIA GeForce GTX 680 and NVIDIA Quadro 8000. The system isfurther extended to one more matrix storage format.
Authors and Affiliations
Aditi V. Kulkarni , Prof. C. R. Barde
Keywords
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