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Optimizing Symmetric Dense Matrix-Vector Multiplication on GPUs
SESSION: Dense Linear Algebra
EVENT TYPE: Paper
TIME: 10:30AM - 11:00AM
AUTHOR(S):Rajib Nath, Stanimire Tomov, Tingxing Dong, Jack Dongarra
ROOM:TCC 305
ABSTRACT: 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