2879

Automatically Tuned Dense Linear Algebra for Multicore+GPU

Xing Fu, Xue Li, Gregory D. Peterson
Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville
Symposium on Application Accelerators in High Performance Computing, 2010

@article{fu2010automatically,

   title={Automatically Tuned Dense Linear Algebra for Multicore+GPU},

   author={Fu, Xing and Li, Xue and Peterson, Gregory D.},

   booktitle={Application Accelerators in High Performance Computing, 2010 Symposium, Papers},

   year={2010}

}

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The Multicore+GPU architecture has been adopted in some of the fastest supercomputers listed on the TOP500. The MAGMA project aims to develop a dense linear algebra library similar to LAPACK but for heterogeneous/hybrid architectures processors like Multicore+GPU. However, to provide portable performance, manual parameter tuning is required. This paper presents automatically tuned LU factorization. The key parameter of LU factorization is tuned automatically to optimize performance for a particular GPU platform. Moreover, we propose a work stealing scheme and GREEN-synchronization to decrease the power consumption of the LU factorization and accelerate the entire application.
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