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Interprocessor communication often dominates the runtime of large matrix computations. We present a parallel algorithm for computing QR decompositions whose bandwidth cost (communication volume) can be decreased at the cost of increasing its latency cost (number of messages). By varying a parameter to navigate the bandwidth/latency tradeoff, we can tune this algorithm for machines with different communication costs
Library software implementing a parallel small-bulge multishift QR algorithm with Aggressive Early D...
Library software implementing a parallel small-bulge multishift QR algorithm with Aggressive Early D...
In many scientific applications, eigenvalues of a matrix have to be computed. By first reducing a ma...
International audienceInterprocessor communication often dominates the runtime of large matrix compu...
International audienceInterprocessor communication often dominates the runtime of large matrix compu...
This paper introduces a new parallel QR decomposition algorithm. The novel load balancing method des...
The increasing complexity of modern computer architectures has greatly influenced algorithm design. ...
n this paper we propose new stable parallel algorithms based on Householder transformations and comp...
QR decomposition is a computationally intensive linear al-gebra operation that factors a matrix A in...
This report addresses several important aspects of parallel implementation of QR decomposition of a ...
Abstract—A systolic array provides an alternative comput-ing paradigm to the von Neuman architecture...
One approach to solving the nonsymmetric eigenvalue problem in parallel is to parallelize the QR alg...
The solution of dense systems of linear equations is at the heart of numerical computations. Such sy...
. The parallel computer CM-200 consists of a very large number of simple processors connected in a m...
In this paper a parallel implementation of the QR algorithm for the eigenvalues of a non-Hermitian m...
Library software implementing a parallel small-bulge multishift QR algorithm with Aggressive Early D...
Library software implementing a parallel small-bulge multishift QR algorithm with Aggressive Early D...
In many scientific applications, eigenvalues of a matrix have to be computed. By first reducing a ma...
International audienceInterprocessor communication often dominates the runtime of large matrix compu...
International audienceInterprocessor communication often dominates the runtime of large matrix compu...
This paper introduces a new parallel QR decomposition algorithm. The novel load balancing method des...
The increasing complexity of modern computer architectures has greatly influenced algorithm design. ...
n this paper we propose new stable parallel algorithms based on Householder transformations and comp...
QR decomposition is a computationally intensive linear al-gebra operation that factors a matrix A in...
This report addresses several important aspects of parallel implementation of QR decomposition of a ...
Abstract—A systolic array provides an alternative comput-ing paradigm to the von Neuman architecture...
One approach to solving the nonsymmetric eigenvalue problem in parallel is to parallelize the QR alg...
The solution of dense systems of linear equations is at the heart of numerical computations. Such sy...
. The parallel computer CM-200 consists of a very large number of simple processors connected in a m...
In this paper a parallel implementation of the QR algorithm for the eigenvalues of a non-Hermitian m...
Library software implementing a parallel small-bulge multishift QR algorithm with Aggressive Early D...
Library software implementing a parallel small-bulge multishift QR algorithm with Aggressive Early D...
In many scientific applications, eigenvalues of a matrix have to be computed. By first reducing a ma...
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