Add to ORKGAbstract. In this paper, we present a novel algorithm of optimal matrix partitioning for parallel dense matrix factorization on heterogeneous processors based on their constant performance model. We prove the correctness of the algorithm and estimate its complexity. We demonstrate that this algorithm better suits extensions to more complicated, non-constant, performance models of heterogeneous processors than traditional algorithms.
Abstract—The paper presents a performance model that can be used to optimally distribute computation...
This paper represents the first attempt towards a decomposition-independent implementation of parall...
This paper is concerned with parallel algorithms for matrix factorization on distributed-memory, mes...
Abstract. The functional performance model (FPM) of heterogeneous proces-sors has proven to be more ...
2012 IEEE 26th Parallel and Distributed Processing Symposium Workshops and PhD Forum (IPDPSW), Shang...
In this report, we consider a simple but important linear algebra kernel, matrix-matrix multiplicati...
Abstract. The paper presents a new data partitioning algorithm for parallel computing on heterogeneo...
The problem of partitioning dense matrices into sets of sub-matrices has received increased attentio...
Matrix Factorization (MF) has been widely applied in machine learning and data mining. Due to the la...
In this paper, we consider the problem of partitioning a square into a set of zones of prescribed ar...
The paper presents a performance model that can be used to optimally distribute computations over he...
Most supercomputers are shipped with both a CPU and a GPU. With the powerful parallel computing capa...
Proceedings of the 8th IEEE International Conference on Cluster Computing (Cluster 2006), October, 2...
In this paper, we address the problem of optimal distribu-tion of computational tasks on a network o...
A number of parallel formulations of dense matrix multiplication algorithm have been developed. For ...
Abstract—The paper presents a performance model that can be used to optimally distribute computation...
This paper represents the first attempt towards a decomposition-independent implementation of parall...
This paper is concerned with parallel algorithms for matrix factorization on distributed-memory, mes...
Abstract. The functional performance model (FPM) of heterogeneous proces-sors has proven to be more ...
2012 IEEE 26th Parallel and Distributed Processing Symposium Workshops and PhD Forum (IPDPSW), Shang...
In this report, we consider a simple but important linear algebra kernel, matrix-matrix multiplicati...
Abstract. The paper presents a new data partitioning algorithm for parallel computing on heterogeneo...
The problem of partitioning dense matrices into sets of sub-matrices has received increased attentio...
Matrix Factorization (MF) has been widely applied in machine learning and data mining. Due to the la...
In this paper, we consider the problem of partitioning a square into a set of zones of prescribed ar...
The paper presents a performance model that can be used to optimally distribute computations over he...
Most supercomputers are shipped with both a CPU and a GPU. With the powerful parallel computing capa...
Proceedings of the 8th IEEE International Conference on Cluster Computing (Cluster 2006), October, 2...
In this paper, we address the problem of optimal distribu-tion of computational tasks on a network o...
A number of parallel formulations of dense matrix multiplication algorithm have been developed. For ...
Abstract—The paper presents a performance model that can be used to optimally distribute computation...
This paper represents the first attempt towards a decomposition-independent implementation of parall...
This paper is concerned with parallel algorithms for matrix factorization on distributed-memory, mes...