Fast Conjugate Gradients with Multiple GPUs

Authors:
Ali Cevahir;Akira Nukada;Satoshi Matsuoka
Affiliations:
Tokyo Institute of Technology,;Tokyo Institute of Technology,;Tokyo Institute of Technology, and National Institute of Informatics, Japan
Venue:
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Year:
2009

Citing 7
Cited 7

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Abstract

The limiting factor for efficiency of sparse linear solvers is the memory bandwidth. In this work, we describe a fast Conjugate Gradient solver for unstructured problems, which runs on multiple GPUs installed on a single mainboard. The solver achieves double precision accuracy with single precision GPUs, using a mixed precision iterative refinement algorithm. To achieve high computation speed, we propose a fast sparse matrix-vector multiplication algorithm, which is the core operation of iterative solvers. The proposed multiplication algorithm efficiently utilizes GPU resources via caching, coalesced memory accesses and load balance between running threads. Experiments on wide range of matrices show that our matrix-vector multiplication algorithm achieves up to 11.6 Gflops on single GeForce 8800 GTS card and CG implementation achieves up to 24.6 Gflops with four GPUs.