AbstractWe have recently multiprocessed a code for the direct solution of sparse linear equations on the Alliant FX/8. We discuss several issues which are involved, all of which are of relevance to any shared memory multiprocessor. Among these issues are the dynamic allocation of data, the management of task queues, task spawning, and the effect of controlling the granularity. We also show runs of our code under the SCHEDULE package from Argonne which presents a portable interface to users of parallel machines, allows the user to define the computational graph, and has very useful graphic output to a SUN workstation. Our tailored code attains a speedup by a factor of about six on the eight processors of the Alliant. We suggest ways of impro...
The ongoing hardware evolution exhibits an escalation in the number, as well as in the heterogeneity...
It is important to have a fast, robust and scalable algorithm to solve a sparse linear system AX=B. ...
We have developed a framework based on relational algebra for compiling efficient sparse matrix cod...
AbstractWe have recently multiprocessed a code for the direct solution of sparse linear equations on...
Vector computers have been extensively used for years in matrix algebra to treat with large dense ma...
Graphics processing units (GPUs) are used as accelerators for algorithms in which the same instructi...
Runtime specialization optimizes programs based on partial information available only at run time. I...
Sparse matrices are first class objects in many VHLLs (very high level languages) used for scientifi...
Gary Kumfert and Alex Pothen have improved the quality and run time of two ordering algorithms for m...
Scientific workloads are often described as directed acyclic task graphs. In this paper, we focus o...
This paper describes two portable packages for general-purpose sparse matrix computations: SPARSKIT...
International audienceTask-based programming models have been widely studied in the context of dense...
This paper presents a new software framework for solving large and sparse linear systems on current ...
Techniques for the vectorization and parallelization of a sequential code for evaluating, one at a t...
We contribute to the optimization of the sparse matrix-vector product by introducing a variant of th...
The ongoing hardware evolution exhibits an escalation in the number, as well as in the heterogeneity...
It is important to have a fast, robust and scalable algorithm to solve a sparse linear system AX=B. ...
We have developed a framework based on relational algebra for compiling efficient sparse matrix cod...
AbstractWe have recently multiprocessed a code for the direct solution of sparse linear equations on...
Vector computers have been extensively used for years in matrix algebra to treat with large dense ma...
Graphics processing units (GPUs) are used as accelerators for algorithms in which the same instructi...
Runtime specialization optimizes programs based on partial information available only at run time. I...
Sparse matrices are first class objects in many VHLLs (very high level languages) used for scientifi...
Gary Kumfert and Alex Pothen have improved the quality and run time of two ordering algorithms for m...
Scientific workloads are often described as directed acyclic task graphs. In this paper, we focus o...
This paper describes two portable packages for general-purpose sparse matrix computations: SPARSKIT...
International audienceTask-based programming models have been widely studied in the context of dense...
This paper presents a new software framework for solving large and sparse linear systems on current ...
Techniques for the vectorization and parallelization of a sequential code for evaluating, one at a t...
We contribute to the optimization of the sparse matrix-vector product by introducing a variant of th...
The ongoing hardware evolution exhibits an escalation in the number, as well as in the heterogeneity...
It is important to have a fast, robust and scalable algorithm to solve a sparse linear system AX=B. ...
We have developed a framework based on relational algebra for compiling efficient sparse matrix cod...