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ͨѶ×÷ÕߣºJack Dongarra
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ÔÚÕâƪ·¢±íÓÚ¡¶¹ú¼Ò¿ÆѧÆÀÂÛ¡·£¨National Science Review£©µÄ perspective ÎÄÕÂÖУ¬×÷ÕßÌá³öÁËÒ»ÖÖеÄÆÀ¼Û±ê×¼£º¸ßÐÔÄܹ²éîÌݶȣ¨ high-performance conjugate gradients£¬HPCG£©¡£
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... Unlike LINPACK, which tests raw floating-point performance and its delivery through the BLAS API, these real-world applications rely on partial differential equations (PDEs) that govern the continuous representations of the physical quantities such as particle speed, momentum, etc. These PDEs involve sparse (not dense) matrices that represent the 3Dl embedding of the discretization mesh. While the size of the sparse data fills the available memory to accommodate the simulation models of interest, most of the optimization techniques that help achieve close to peak performance in dense matrix calculations are only marginally useful in the context of sparse matrices originating from PDEs.
Our new benchmark, called high-performance conjugate gradients (HPCG) (further information is available at www.hpcg-benchmark.org), is based on Mantevo collection's HPCCG code base, but aims to go beyond its originator and represent the calculations that commonly occur during the numerical solution of PDEs in modern state-of-the-art solvers. To that end, HPCG is dominated by sparse operations such as sparse matrix-vector product and sparse matrix triangular solve. ...
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