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Solving Sparse Linear Systems on NVIDIA Tesla GPUs

Mingliang Wang, Hector Klie, Manish Parashar, Hari Sudan
NSF Center for Autonomic Computing (CAC) The Applied Software System Laboratory (TASSL) Rutgers, The State University of New Jersey, Piscataway, USA NJ 08854
Computational Science – ICCS 2009 (2009), pp. 864-873

@article{wang2009solving,

   title={Solving sparse linear systems on NVIDIA Tesla GPUs},

   author={Wang, M. and Klie, H. and Parashar, M. and Sudan, H.},

   journal={Computational Science–ICCS 2009},

   pages={864–873},

   year={2009},

   publisher={Springer}

}

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Current many-core GPUs have enormous processing power, and unlocking this power for general-purpose computing is very attractive due to their low cost and efficient power utilization. However, the fine-grained parallelism and the stream-programming model supported by these GPUs require a paradigm shift, especially for algorithm designers. In this paper we present the design of a GPU-based sparse linear solver using the Generalized Minimum RESidual (GMRES) algorithm in the CUDA programming environment. Our implementation achieved a speedup of over 20x on the Tesla T10P based GTX280 GPU card for benchmarks with from a few thousands to a few millions unknowns.
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