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PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
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DTSTART:20141118T231500Z
DTEND:20141119T010000Z
LOCATION:New Orleans Theater Lobby
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: Sparse matrix-vector and multiple-vector multiplications (SpMV and SpMM) are performance bottlenecks in numerous applications. We implemented two SpMM kennels to integrate in our library of auto-tuned kernels for GPUs. Our kernels use registers to exploit data reuse in SpMM. DIA-SpMM targets structured matrices and ELL-SpMM targets matrices with uniform row lengths. Work is continuing on SpMM kernels for unstructured matrices.=0A=0AExecuting on NVIDIA Kepler Tesla K40m, DIA-SpMM is 2.4x faster than NVIDIA CUSP DIA-SpMV. ELL-SpMM is 2.8x faster than CUSP ELL-SpMV.=0A=0ADIA-SpMM is 5.2x faster than the highly optimized NVIDIA CUSPARSE CSR-SpMV. The maximum speedup is 6.5x. ELL-SpMM is 3.9x faster than CUSPARSE CSR-SpMV. The maximum speedup is 8.3x.=0A=0ADIA-SpMM is 2x faster than CUSPARSE CSR-SpMM. ELL-SpMM is 1.6x faster.=0A=0AFor structured matrices, DIA-SpMM on the K40m GPU is 7.2x faster than Intel MKL CSR-SpMV on a dual socket 10-core Intel Ivy Bridge E5-2690. The maximum speedup is 12.3x.
SUMMARY:Performance of Sparse Matrix-Multiple Vectors Multiplication on Multicore and GPUs
PRIORITY:3
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