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Abstract
In this study, we present an optimization sparse approximate inverse (SPAI) preconditioning algorithm on GPU, called GSPAI-Opt. In GSPAI-Opt, it fuses the advantages of two popular SPAI preconditioning algorithms, and has the following novelties: (1) an optimization strategy is proposed to choose whether to use the constant or non-constant thread group for any sparse pattern of the preprocessor, and (2) a parallel framework of optimizing the SPAI preconditioner is proposed on GPU, and (3) for each component of the preconditioner, a decision tree is established to choose the optimal kernel of computing it. Experimental results validate the effectiveness of GSPAI-Opt.
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