Add to ORKGWe provide parallel matrix-vector multiply routines for 1D and 2D partitioned sparse square and rectangular matrices. We clearly give pseu-docodes that perform necessary initializations for parallel execution. We show how to maximize overlapping between communication and compu-tation through the proper usage of compressed sparse row and compressed sparse column formats of the sparse matrices. We give pseudocodes for multiplication routines which benefit from such overlaps
One-dimensional (1D) partitioning of sparse matrices results in lower quality partitioning than two-...
This paper addresses the problem of one-dimensional partitioning of structurally unsymmetric square ...
Vector computers have been extensively used for years in matrix algebra to treat with large dense ma...
This paper describes two portable packages for general-purpose sparse matrix computations: SPARSKIT...
The matrix-vector product is one of the most important computational components of Krylov methods. T...
International audienceThere are three common parallel sparse matrix-vector multiply algorithms: 1D 3...
A new method is presented for distributing data in sparse matrix-vector multiplication. The method i...
We identify the challenges that are special to parallel sparse matrix-matrix multiplication (PSpGEMM...
We design and develop a work-efficient multithreaded algorithm for sparse matrix-sparse vector multi...
We design and develop a work-efficient multithreaded algorithm for sparse matrix-sparse vector multi...
The sparse matrix--vector multiplication is an important kernel, but is hard to efficiently execute ...
The sparse matrix--vector multiplication is an important kernel, but is hard to efficiently execute ...
International audienceWe propose a novel sparse matrix partitioning scheme, called semi-two-dimensio...
A new method is presented for distributing data in sparse matrix-vector multiplication. The method i...
Abstract. This paper addresses the problem of one-dimensional partitioning of structurally unsymmetr...
One-dimensional (1D) partitioning of sparse matrices results in lower quality partitioning than two-...
This paper addresses the problem of one-dimensional partitioning of structurally unsymmetric square ...
Vector computers have been extensively used for years in matrix algebra to treat with large dense ma...
This paper describes two portable packages for general-purpose sparse matrix computations: SPARSKIT...
The matrix-vector product is one of the most important computational components of Krylov methods. T...
International audienceThere are three common parallel sparse matrix-vector multiply algorithms: 1D 3...
A new method is presented for distributing data in sparse matrix-vector multiplication. The method i...
We identify the challenges that are special to parallel sparse matrix-matrix multiplication (PSpGEMM...
We design and develop a work-efficient multithreaded algorithm for sparse matrix-sparse vector multi...
We design and develop a work-efficient multithreaded algorithm for sparse matrix-sparse vector multi...
The sparse matrix--vector multiplication is an important kernel, but is hard to efficiently execute ...
The sparse matrix--vector multiplication is an important kernel, but is hard to efficiently execute ...
International audienceWe propose a novel sparse matrix partitioning scheme, called semi-two-dimensio...
A new method is presented for distributing data in sparse matrix-vector multiplication. The method i...
Abstract. This paper addresses the problem of one-dimensional partitioning of structurally unsymmetr...
One-dimensional (1D) partitioning of sparse matrices results in lower quality partitioning than two-...
This paper addresses the problem of one-dimensional partitioning of structurally unsymmetric square ...
Vector computers have been extensively used for years in matrix algebra to treat with large dense ma...