In this paper, we address the issue of imple-menting matrix-matrix multiplication on heteroge-neous platforms. We target two different classes of heterogeneous computing resources: heterogeneous networks of workstations, and collections of het-erogeneous clusters. Intuitively, the problem is to load balance the work with different-speed re-sources while minimizing the communication vol-ume. We formally state this problem and prove its NP-completeness. Next we introduce a (poly-nomial) column-based heuristic, which turns out to be very satisfactory: we derive a theoretical perfor-mance guarantee for the heuristic, and we assess its practical usefulness through MPI experiments.
Parallel sparse matrix-matrix multiplication algorithms (PSpGEMM) spend most of their running time o...
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and...
International audienceIn this paper, we deal with two geometric problems arising from heterogeneous ...
International audienceIn this paper, we address the issue of implementing matrix-matrix multiplicati...
(eng) In this paper, we address the issue of implementing matrix-matrix multiplication on heterogene...
Proceedings of the 8th IEEE International Conference on Cluster Computing (Cluster 2006), October, 2...
In this paper, an adaptive matrix multiplication algorithm for dynamic heterogeneous environments is...
This paper presents and analyzes two different strategies of heterogeneous distribution of computati...
In this report, we consider a simple but important linear algebra kernel, matrix-matrix multiplicati...
Multiplication of a sparse matrix with a dense matrix is a building block of an increasing number of...
In this document, we describe two strategies of distribution of computations that can be used to imp...
We present a new approach to utilizing all CPU cores and all GPUs on heterogeneous multicore and mul...
2012 IEEE 26th Parallel and Distributed Processing Symposium Workshops and PhD Forum (IPDPSW), Shang...
In this paper we present an efficient dense matrix multi-plication algorithm for distributed memory ...
The distributed matrix multiplication problem with an unknown number of stragglers is considered, wh...
Parallel sparse matrix-matrix multiplication algorithms (PSpGEMM) spend most of their running time o...
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and...
International audienceIn this paper, we deal with two geometric problems arising from heterogeneous ...
International audienceIn this paper, we address the issue of implementing matrix-matrix multiplicati...
(eng) In this paper, we address the issue of implementing matrix-matrix multiplication on heterogene...
Proceedings of the 8th IEEE International Conference on Cluster Computing (Cluster 2006), October, 2...
In this paper, an adaptive matrix multiplication algorithm for dynamic heterogeneous environments is...
This paper presents and analyzes two different strategies of heterogeneous distribution of computati...
In this report, we consider a simple but important linear algebra kernel, matrix-matrix multiplicati...
Multiplication of a sparse matrix with a dense matrix is a building block of an increasing number of...
In this document, we describe two strategies of distribution of computations that can be used to imp...
We present a new approach to utilizing all CPU cores and all GPUs on heterogeneous multicore and mul...
2012 IEEE 26th Parallel and Distributed Processing Symposium Workshops and PhD Forum (IPDPSW), Shang...
In this paper we present an efficient dense matrix multi-plication algorithm for distributed memory ...
The distributed matrix multiplication problem with an unknown number of stragglers is considered, wh...
Parallel sparse matrix-matrix multiplication algorithms (PSpGEMM) spend most of their running time o...
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and...
International audienceIn this paper, we deal with two geometric problems arising from heterogeneous ...