From Sparse Matrix to Optimal GPU CUDA Sparse Matrix Vector Product Implementation

Authors:
Ahmed H. El Zein;Alistair P. Rendell
Affiliations:
-;-
Venue:
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Year:
2010

Citing 5
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Abstract

The CUDA model for GPUs presents the programmer with a plethora of different programming options. These includes different memory types, different memory access methods, and different data types. Identifying which options to use and when is a non-trivial exercise. This paper explores the effect of these different options on the performance of a routine that evaluates sparse matrix vector products. A process for analysing performance and selecting the subset of implementations that perform best is proposed. The potential for mapping sparse matrix attributes to optimal CUDA sparse matrix vector product implementation is discussed.