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EECS Publication

An Improved MAGMA GEMM for Fermi GPUs

Rajib Nath, Stanimire Tomov, and Jack Dongarra

We present an improved matrix-matrix multiplication routine (GEMM) in the MAGMA BLAS library that targets the Fermi GPUs. We show how to modify the previous MAGMA GEMM kernels in order to make a more efficient use of the Fermi's new architectural features, most notably their extended memory hierarchy and sizes. The improved kernels run at up to 300 GFlop/s in double and up to 600 GFlop/s in single precision arithmetic (on a C2050), which is 58% of the theoretical peak. We compare the improved kernels with the currently available in CUBLAS 3.1. Further, we show the effect of the new kernels on higher level dense linear algebra (DLA) routines such as the one-sided matrix factorizations, and compare their performances with corresponding, currently available routines running on homogeneous multi-core systems. A general conclusion is that DLA has become a better fit for the new GPU architectures, to the point where DLA can run more efficiently on GPUs than on current, high-end homogeneous multi-core-based systems.

Published  2010-07-29 04:00:00  as  ut-cs-10-655 (ID:57)


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