A framework for general sparse matrix–matrix multiplication on GPUs and heterogeneous processors
نویسندگان
چکیده
منابع مشابه
A Framework for General Sparse Matrix-Matrix Multiplication on GPUs and Heterogeneous Processors
General sparse matrix-matrix multiplication (SpGEMM) is a fundamental building block for numerous applications such as algebraic multigrid method (AMG), breadth first search and shortest path problem. Compared to other sparse BLAS routines, an efficient parallel SpGEMM implementation has to handle extra irregularity from three aspects: (1) the number of nonzero entries in the resulting sparse m...
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We are witnessing the emergence of Graphics Processor units (GPUs) as powerful massively parallel systems. Furthermore, the introduction of new APIs for general-purpose computations on GPUs, namely CUDA from NVIDIA, Stream SDK from AMD, and OpenCL, makes GPUs an attractive choice for high-performance numerical and scientific computing. Sparse Matrix-Vector multiplication (SpMV) is one of the mo...
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ژورنال
عنوان ژورنال: Journal of Parallel and Distributed Computing
سال: 2015
ISSN: 0743-7315
DOI: 10.1016/j.jpdc.2015.06.010