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258,528
Parallel Sparse MatrixVector Multiplication
, 1997
"... In this paper we describe an algorithm for unstructured sparse matrixvector multiplication on distributed memory parallel computers. We focus on both local and global computational efficiency, i.e. single processor computational performance and interprocessor communication efficiency. Numerical ex ..."
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Cited by 2 (0 self)
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In this paper we describe an algorithm for unstructured sparse matrixvector multiplication on distributed memory parallel computers. We focus on both local and global computational efficiency, i.e. single processor computational performance and interprocessor communication efficiency. Numerical
Sparse matrixvector multiplication on FPGAs
 In Proceedings of the ACM International Symposium on Field Programmable Gate Arrays
, 2005
"... Sparse matrixvector multiplication (SpMXV) is a key computational kernel widely used in scientific applications and signal processing applications. However, the performance of SpMXV on most modern processors is poor due to the irregular sparsity structure in the matrices. Applicationspecific proce ..."
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Cited by 60 (7 self)
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Sparse matrixvector multiplication (SpMXV) is a key computational kernel widely used in scientific applications and signal processing applications. However, the performance of SpMXV on most modern processors is poor due to the irregular sparsity structure in the matrices. Application
Sparse MatrixVector Multiplication on FPGAs
, 2007
"... Floatingpoint Sparse MatrixVector Multiplication (SpMXV) is a key computational kernel in scientific and engineering applications. The poor data locality of sparse matrices significantly reduces the performance of SpMXV on generalpurpose processors, which rely heavily on the cache hierarchy to ac ..."
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Floatingpoint Sparse MatrixVector Multiplication (SpMXV) is a key computational kernel in scientific and engineering applications. The poor data locality of sparse matrices significantly reduces the performance of SpMXV on generalpurpose processors, which rely heavily on the cache hierarchy
On Improving the Performance of Sparse MatrixVector Multiplication
 In Proceedings of the International Conference on HighPerformance Computing
, 1997
"... We analyze singlenode performance of sparse matrixvector multiplication by investigating issues of data locality and finegrained parallelism. We examine the datalocality characteristics of the compressedsparse row representation and consider improvements in locality through matrix permutation. ..."
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Cited by 28 (0 self)
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We analyze singlenode performance of sparse matrixvector multiplication by investigating issues of data locality and finegrained parallelism. We examine the datalocality characteristics of the compressedsparse row representation and consider improvements in locality through matrix permutation
Vector ISA Extension for Sparse MatrixVector Multiplication
"... . In this paper we introduce a vector ISA extension to facilitate sparse matrix manipulation on vector processors (VPs). First we introduce a new Block Based Compressed Storage (BBCS) format for sparse matrix representation and a Blockwise Sparse MatrixVector Multiplication approach. Additionally, ..."
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. In this paper we introduce a vector ISA extension to facilitate sparse matrix manipulation on vector processors (VPs). First we introduce a new Block Based Compressed Storage (BBCS) format for sparse matrix representation and a Blockwise Sparse MatrixVector Multiplication approach. Additionally
“Implementing Sparse MatrixVector Multiplication on
"... Minimize memory traffic Maximize coalesced memory access ..."
Vector ISA Extension for Sparse MatrixVector Multiplication
"... . In this paper we introduce a vector ISA extension to facilitate sparse matrix manipulation on vector processors (VPs). First we introduce a new Block Based Compressed Storage (BBCS) format for sparse matrix representation and a Blockwise Sparse MatrixVector Multiplication approach. Additionally, ..."
Abstract
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. In this paper we introduce a vector ISA extension to facilitate sparse matrix manipulation on vector processors (VPs). First we introduce a new Block Based Compressed Storage (BBCS) format for sparse matrix representation and a Blockwise Sparse MatrixVector Multiplication approach. Additionally
A New Format for the Sparse Matrixvector Multiplication
"... Algorithms for the sparse matrixvector multiplication (shortly SpMV) are important building blocks in solvers of sparse systems of linear equations. Due to matrix sparsity, the memory access patterns are irregular and the utilization of a cache suffers from low spatial and temporal locality. To red ..."
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Algorithms for the sparse matrixvector multiplication (shortly SpMV) are important building blocks in solvers of sparse systems of linear equations. Due to matrix sparsity, the memory access patterns are irregular and the utilization of a cache suffers from low spatial and temporal locality
Understanding the performance of sparse matrixvector multiplication
 In PDP ’08: Proceedings of the 16th Euromicro International Conference on Parallel, Distributed and Networkbased Processing
, 2008
"... In this paper we revisit the performance issues of the widely used sparse matrixvector multiplication (SpMxV) kernel on modern microarchitectures. Previous scientific work reports a number of different factors that may significantly reduce performance. However, the interaction of these factors with ..."
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Cited by 11 (6 self)
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In this paper we revisit the performance issues of the widely used sparse matrixvector multiplication (SpMxV) kernel on modern microarchitectures. Previous scientific work reports a number of different factors that may significantly reduce performance. However, the interaction of these factors
Improving Performance of Sparse MatrixVector Multiplication
, 1999
"... Sparse matrixvector multiplication (SpMxV) is one of the most important computational kernels in scientific computing. It often suffers from poor cache utilization and extra load operations because of memory indirections used to exploit sparsity. We propose alternative data structures, along with r ..."
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Cited by 65 (3 self)
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Sparse matrixvector multiplication (SpMxV) is one of the most important computational kernels in scientific computing. It often suffers from poor cache utilization and extra load operations because of memory indirections used to exploit sparsity. We propose alternative data structures, along
Results 1  10
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