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Rescheduling for Locality in Sparse Matrix Computations
 Proceedings of the 2001 International Conference on Computational Science, Lecture Notes in Computer Science
"... . In modern computer architecture the use of memory hierarchies causes a program's data locality to directly affect performance. Data locality occurs when a piece of data is still in a cache upon reuse. For dense matrix computations, loop transformations can be used to improve data locality ..."
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Cited by 20 (8 self)
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locality. However, sparse matrix computations have nonaffine loop bounds and indirect memory references which prohibit the use of compile time loop transformations. This paper describes an algorithm to tile at runtime called serial sparse tiling. We test a runtime tiled version of sparse Gauss
SPARSKIT: a basic tool kit for sparse matrix computations  Version 2
, 1994
"... . This paper presents the main features of a tool package for manipulating and working with sparse matrices. One of the goals of the package is to provide basic tools to facilitate exchange of software and data between researchers in sparse matrix computations. Our starting point is the Harwell/Boei ..."
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Cited by 314 (22 self)
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. This paper presents the main features of a tool package for manipulating and working with sparse matrices. One of the goals of the package is to provide basic tools to facilitate exchange of software and data between researchers in sparse matrix computations. Our starting point is the Harwell
Tiling of Iterative Sparse Matrix Computations
"... In modern computer architecture the use of memory hierarchies allows a program's data locality to directly affect performance. Data locality occurs when data is still in a cache upon reuse. This paper presents a technique for tiling iterative sparse matrix computations in order to improve the ..."
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In modern computer architecture the use of memory hierarchies allows a program's data locality to directly affect performance. Data locality occurs when data is still in a cache upon reuse. This paper presents a technique for tiling iterative sparse matrix computations in order to improve
Sparse Matrix Computations on Clusters with GPGPUs
"... AbstractHybrid nodes containing GPUs are rapidly becoming the norm in parallel machines. We have conducted some experiments regarding how to plug GPUenabled computational kernels into PSBLAS, a MPIbased library specifically geared towards sparse matrix computations. In this paper, we present our ..."
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AbstractHybrid nodes containing GPUs are rapidly becoming the norm in parallel machines. We have conducted some experiments regarding how to plug GPUenabled computational kernels into PSBLAS, a MPIbased library specifically geared towards sparse matrix computations. In this paper, we present
Predicting Structure In Sparse Matrix Computations
 SIAM J. Matrix Anal. Appl
, 1994
"... . Many sparse matrix algorithmsfor example, solving a sparse system of linear equationsbegin by predicting the nonzero structure of the output of a matrix computation from the nonzero structure of its input. This paper is a catalog of ways to predict nonzero structure. It contains known result ..."
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Cited by 50 (5 self)
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. Many sparse matrix algorithmsfor example, solving a sparse system of linear equationsbegin by predicting the nonzero structure of the output of a matrix computation from the nonzero structure of its input. This paper is a catalog of ways to predict nonzero structure. It contains known
Compilation Techniques for Sparse Matrix Computations
 In Proceedings of the 1993 International Conference on Supercomputing
, 1993
"... The problem of compiler optimization of sparse codes is well known and no satisfactory solutions have been found yet. One of the major obstacles is formed by the fact that sparse programs deal explicitly with the particular data structures selected for storing sparse matrices. This explicit data str ..."
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Cited by 37 (5 self)
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code optimization with explicit data structure selection. Not only enables this method the compiler to generate efficient code for sparse computations, also the task of the programmer is greatly reduced in complexity. Index Terms: Compilation Techniques, Optimization, Program Transformations
Sparse matrix computations on reconfigurable hardware
 Computer
, 2007
"... Using a highlevellanguage to hardwaredescriptionlanguage compiler and some novel architectures and algorithms to map two wellknown doubleprecision floatingpoint sparse matrix iterativelinearequation solvers—the Jacobi and conjugate gradient methods—onto a reconfigurable computer achieves mo ..."
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Cited by 7 (1 self)
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Using a highlevellanguage to hardwaredescriptionlanguage compiler and some novel architectures and algorithms to map two wellknown doubleprecision floatingpoint sparse matrix iterativelinearequation solvers—the Jacobi and conjugate gradient methods—onto a reconfigurable computer achieves
Tools and Libraries for Parallel Sparse Matrix Computations
, 1995
"... This paper describes two portable packages for generalpurpose sparse matrix computations: SPARSKIT and P SPARSLIB. Their emphasis is on iterative techniques, with the latter also emphasizing parallel computation. The packages are a collection of tools which may be used either as a library, or as ..."
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This paper describes two portable packages for generalpurpose sparse matrix computations: SPARSKIT and P SPARSLIB. Their emphasis is on iterative techniques, with the latter also emphasizing parallel computation. The packages are a collection of tools which may be used either as a library
Lecture 28: Sparse matrix computations
"... A sparse matrix is a matrix with relatively few nonzero elements. Often the number of nonzeroes amounts to much less than 1%. Sparse matrices arises in describing network problems in matrix form. The matrix typically captures the relationship between variables in different nodes of the network. The ..."
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A sparse matrix is a matrix with relatively few nonzero elements. Often the number of nonzeroes amounts to much less than 1%. Sparse matrices arises in describing network problems in matrix form. The matrix typically captures the relationship between variables in different nodes of the network
Results 1  10
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