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SCHEDULE: NOV 12-18, 2011

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Optimizing Symmetric Dense Matrix-Vector Multiplication on GPUs

SESSION: Dense Linear Algebra


TIME: 10:30AM - 11:00AM

AUTHOR(S):Rajib Nath, Stanimire Tomov, Tingxing Dong, Jack Dongarra


GPUs are an excellent accelerator for data-parallel applications with regular data access patterns. It is challenging, however, to optimize computations with irregular data access patterns on GPUs. One such computation is the Symmetric Matrix Vector product (SYMV) for dense linear algebra. Optimizing the SYMV kernel is important be- cause it forms the basis of fundamental algorithms such as linear solvers and eigenvalue solvers on symmetric matrices. In this work, we present a new algorithm for optimizing the SYMV kernel on GPUs. Our optimized SYMV in single precision brings up to 7X speed up compared to the (latest) CUBLAS 3.2 NVIDIA library on the GTX 280 GPU. Our SYMV kernel tuned for Fermi C2050 is 4.5X faster than CUBLAS 3.2 in single precision.

Chair/Author Details:

Rajib Nath - University of California, San Diego

Stanimire Tomov - University of Tennessee, Knoxville

Tingxing Dong - University of Tennessee, Knoxville

Jack Dongarra - University of Tennessee, Knoxville

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The full paper can be found in the ACM Digital Library

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