Automatically Tuned Dense Linear Algebra for Multicore+GPU
نویسندگان
چکیده
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 GREENsynchronization to decrease the power consumption of the LU factorization and accelerate the entire application.
منابع مشابه
Towards dense linear algebra for hybrid GPU accelerated manycore systems
0167-8191/$ see front matter 2010 Elsevier B.V doi:10.1016/j.parco.2009.12.005 * Corresponding author. Tel.: +1 865 974 8295; fa E-mail addresses: [email protected] (S. Tomov We highlight the trends leading to the increased appeal of using hybrid multicore + GPU systems for high performance computing. We present a set of techniques that can be used to develop efficient dense linear algebra alg...
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تاریخ انتشار 2010