Benchmarking of GPU NVIDIA CUDA, CUBLAS and MAGMA Libraries Based on Matrix Multiplication Problem
Keywords:
Parallel computing, Matrix algebra, Graphical processorAbstract
Solving linear systems of equations is a fundamental problem in scientific computing. Many scientific computer applications need a high-performance matrix algebra. The major hardware developments always influenced the new developments in linear algebra libraries. Nowadays major chip manufacturers are developing next-generation microprocessor designs that integrate multicore CPU and GPU components [1]. The main aim is to benchmark CUBLAS and MAGMA libraries on matrix multiplication problem using the Tesla C1060 graphical processing unit.
References
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J. Nickolls, I. Buck, M. Garland and K. Skadron,“Scalable parallel programming with CUDA”, Presentation by Christian Hansen Article Published in ACM Queue, March, page 2, 2008.
S.Tomov, R.Nath, H. Ltaief and J. Dongarra,“Dense linear algebra solvers for multicore with GPU accelerators”, Proc. of IPDPS'10, Atlanta, GA, January 15, pp. 1—2. 2010.
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