A Unified Sparse Matrix Data Format for Efficient General Sparse Matrix-Vector Multiplication on Modern Processors with Wide SIMD Units
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چکیده
منابع مشابه
A Unified Sparse Matrix Data Format for Efficient General Sparse Matrix-Vector Multiplication on Modern Processors with Wide SIMD Units
Sparse matrix-vector multiplication (spMVM) is the most time-consuming kernel in many numerical algorithms and has been studied extensively on all modern processor and accelerator architectures. However, the optimal sparse matrix data storage format is highly hardware-specific, which could become an obstacle when using heterogeneous systems. Also, it is as yet unclear how the wide single instru...
متن کاملA unified sparse matrix data format for modern processors with wide SIMD units
Sparse matrix-vector multiplication (spMVM) is the most time-consuming kernel in many numerical algorithms and has been studied extensively on all modern processor and accelerator architectures. However, the optimal sparse matrix data storage format is highly hardware-specific, which could become an obstacle when using heterogeneous systems. Also, it is as yet unclear how the wide single instru...
متن کاملA Unified Sparse Matrix Data Format for Efficient General Sparse Matrix-Vector Multiplication on Modern Processors with Wide SIMD Units | SIAM Journal on Scientific Computing | Vol. 36, No. 5 | Society for Industrial and Applied Mathematics
Sparse matrix-vector multiplication (spMVM) is the most time-consuming kernel in many numerical algorithms and has been studied extensively on all modern processor and accelerator architectures. However, the optimal sparse matrix data storage format is highly hardware-specific, which could become an obstacle when using heterogeneous systems. Also, it is as yet unclear how the wide single instru...
متن کاملEfficient Sparse Matrix-Vector Multiplication on CUDA
The massive parallelism of graphics processing units (GPUs) offers tremendous performance in many high-performance computing applications. While dense linear algebra readily maps to such platforms, harnessing this potential for sparse matrix computations presents additional challenges. Given its role in iterative methods for solving sparse linear systems and eigenvalue problems, sparse matrix-v...
متن کاملBlock-Row Sparse Matrix-Vector Multiplication on SIMD Machines
The irregular nature of the data structures required to efficiently store arbitrary sparse matrices and the architectural constraints of a SIMD computer make it difficult to design an algorithm that can efficiently multiply an arbitrary sparse matrix by a vector. A new ‘‘block-row’’ algorithm is proposed. It allows the ‘‘regularity’’ of a data structure with a row-major mapping to be varied by ...
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ژورنال
عنوان ژورنال: SIAM Journal on Scientific Computing
سال: 2014
ISSN: 1064-8275,1095-7197
DOI: 10.1137/130930352