Hybrid GPU/CPU clusters are becoming very popular in the scientific computing community, as attested by the number of such systems present in the Top 500 list. In this paper, we address one of the key algorithms for scientific applications: the computation of sparse matrix-vector products that lies at the heart of iterative solvers for sparse linear systems.
We detail how design patterns for sparse matrix computations enable us to easily adapt to such a heterogeneous GPU/CPU platform using several sparse matrix formats in order to achieve best performance; then, we analyze static load balancing strategies for devising a suitable data decomposition and propose our approach. We discuss our experience in using different sparse matrix formats and data partitioning algorithms with a number of computational experiments executed on three different hybrid GPU/CPU platforms.
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