We present a benchmark of iterative solvers for sparse matrices. The benchmark contains several common methods and data structures, chosen to be representative of the performance of a large class of methods in current use. We give results on some high performance processors that show that performance is largely determined by memory bandwidth
Abstract. This paper presents uniprocessor performance optimizations, automatic tuning techniques, a...
Abstract. Sparse matrix-vector multiplication is an important computational kernel that tends to per...
A technique for optimizing software is proposed that involves the use of a standardized set of compu...
revision 2001/06/01 We present a benchmark of iterative solvers for sparse matrices. The benchmark c...
AbstractMany iterative solvers and preconditioners have recently been proposed for linear iterative ...
Solving large-scale systems of linear equations [] { } {}bxA = is one of the most expensive and cr...
Manufacturers of computer hardware are able to continuously sustain an unprecedented pace of progres...
Sparse matrix computations play an important role in iterative methods to solve systems of equations...
This paper describes two portable packages for general-purpose sparse matrix computations: SPARSKIT...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
The standard LU factorization-based solution process for linear systems can be enhanced in speed or ...
This book describes, in a basic way, the most useful and effective iterative solvers and appropriate...
Due to the character of the original source materials and the nature of batch digitization, quality ...
The emergence of multicore architectures and highly scalable platforms motivates the development of ...
As modern supercomputers approach the Exascale, many numerical libraries face scalability issues due...
Abstract. This paper presents uniprocessor performance optimizations, automatic tuning techniques, a...
Abstract. Sparse matrix-vector multiplication is an important computational kernel that tends to per...
A technique for optimizing software is proposed that involves the use of a standardized set of compu...
revision 2001/06/01 We present a benchmark of iterative solvers for sparse matrices. The benchmark c...
AbstractMany iterative solvers and preconditioners have recently been proposed for linear iterative ...
Solving large-scale systems of linear equations [] { } {}bxA = is one of the most expensive and cr...
Manufacturers of computer hardware are able to continuously sustain an unprecedented pace of progres...
Sparse matrix computations play an important role in iterative methods to solve systems of equations...
This paper describes two portable packages for general-purpose sparse matrix computations: SPARSKIT...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
The standard LU factorization-based solution process for linear systems can be enhanced in speed or ...
This book describes, in a basic way, the most useful and effective iterative solvers and appropriate...
Due to the character of the original source materials and the nature of batch digitization, quality ...
The emergence of multicore architectures and highly scalable platforms motivates the development of ...
As modern supercomputers approach the Exascale, many numerical libraries face scalability issues due...
Abstract. This paper presents uniprocessor performance optimizations, automatic tuning techniques, a...
Abstract. Sparse matrix-vector multiplication is an important computational kernel that tends to per...
A technique for optimizing software is proposed that involves the use of a standardized set of compu...
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