AbstractÐIn this paper, we address the issue of implementing matrix multiplication on heterogeneous platforms. We target two different classes of heterogeneous computing resources: heterogeneous networks of workstations and collections of heterogeneous clusters. Intuitively, the problem is to load balance the work with different speed resources while minimizing the communication volume. We formally state this problem in a geometric framework and prove its NP-completeness. Next, we introduce a (polynomial) column-based heuristic, which turns out to be very satisfactory: We derive a theoretical performance guarantee for the heuristic and we assess its practical usefulness through MPI experiments. Index TermsÐParallel algorithms, load balancin...
Matrix multiplication is taken as a test bed for parallel processing on heterogeneous networks of wo...
The problem of partitioning dense matrices into sets of sub-matrices has received increased attentio...
In this document, we describe two strategies of distribution of computations that can be used to imp...
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...
In this paper, we address the issue of imple-menting matrix-matrix multiplication on heteroge-neous ...
In this paper, an adaptive matrix multiplication algorithm for dynamic heterogeneous environments is...
Proceedings of the 8th IEEE International Conference on Cluster Computing (Cluster 2006), October, 2...
We present a new approach to utilizing all CPU cores and all GPUs on heterogeneous multicore and mul...
Matrix multiplication is one of the important operations in scientific and engineering application. ...
Parallel computing on networks of workstations are intensively used in some application areas such a...
Proceedings of: Third International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2016...
Parallel computing on networks of workstations are intensively used in some application areas such a...
In this paper we present an efficient dense matrix multi-plication algorithm for distributed memory ...
This paper presents and analyzes two different strategies of heterogeneous distribution of computati...
Matrix multiplication is taken as a test bed for parallel processing on heterogeneous networks of wo...
The problem of partitioning dense matrices into sets of sub-matrices has received increased attentio...
In this document, we describe two strategies of distribution of computations that can be used to imp...
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...
In this paper, we address the issue of imple-menting matrix-matrix multiplication on heteroge-neous ...
In this paper, an adaptive matrix multiplication algorithm for dynamic heterogeneous environments is...
Proceedings of the 8th IEEE International Conference on Cluster Computing (Cluster 2006), October, 2...
We present a new approach to utilizing all CPU cores and all GPUs on heterogeneous multicore and mul...
Matrix multiplication is one of the important operations in scientific and engineering application. ...
Parallel computing on networks of workstations are intensively used in some application areas such a...
Proceedings of: Third International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2016...
Parallel computing on networks of workstations are intensively used in some application areas such a...
In this paper we present an efficient dense matrix multi-plication algorithm for distributed memory ...
This paper presents and analyzes two different strategies of heterogeneous distribution of computati...
Matrix multiplication is taken as a test bed for parallel processing on heterogeneous networks of wo...
The problem of partitioning dense matrices into sets of sub-matrices has received increased attentio...
In this document, we describe two strategies of distribution of computations that can be used to imp...