Abstract—Two strategies of distribution of computations can be used to implement parallel solvers for dense linear algebra prob-lems for Heterogeneous Computational Clusters of Multicore Processors (HCoMs). These strategies are called Heterogeneous Process Distribution Strategy (HPS) and Heterogeneous Data Distribution Strategy (HDS). They are not novel and have been researched thoroughly. However, the advent of multicores neces-sitates enhancements to them. In this paper, we present these enhancements. Our study is based on experiments using six ap-plications to perform Parallel Matrix-matrix Multiplication (PMM) on an HCoM employing the two distribution strategies. Keywords- Heterogeneous ScaLAPACK; HeteroMPI; multicore clusters; matrix-m...
Abstract. The paper presents a tool that ports ScaLAPACK programs designed to run on massively paral...
(eng) In this paper, we address the issue of implementing matrix-matrix multiplication on heterogene...
The aim of data and task parallel scheduling for dense linear algebra kernels is to minimize the pro...
In this document, we describe two strategies of distribution of computations that can be used to imp...
In this document, we describe two strategies of distribution of computations that can be used to imp...
This paper presents and analyzes two different strategies of heterogeneous distribution of computati...
International audienceThis paper discusses some algorithmic issues when computing with a heterogeneo...
This paper discusses some algorithmic issues when computing with a heterogeneous network of workstat...
This paper presents a self-optimization methodology for parallel linear algebra rou-tines on heterog...
This paper describes the design and the implementation of parallel routines in the Heterogeneous Sca...
(eng) We study the implementation of dense linear algebra computations, such as matrix multiplicatio...
We present a new approach to utilizing all CPU cores and all GPUs on heterogeneous multicore and mul...
The current state and foreseeable future of high performance scientific computing (HPC) can be descr...
International audienceWe study the implementation of dense linear algebra computations, such as matr...
This paper presents a package, called Heterogeneous PBLAS (HeteroPBLAS), which is built on top of PB...
Abstract. The paper presents a tool that ports ScaLAPACK programs designed to run on massively paral...
(eng) In this paper, we address the issue of implementing matrix-matrix multiplication on heterogene...
The aim of data and task parallel scheduling for dense linear algebra kernels is to minimize the pro...
In this document, we describe two strategies of distribution of computations that can be used to imp...
In this document, we describe two strategies of distribution of computations that can be used to imp...
This paper presents and analyzes two different strategies of heterogeneous distribution of computati...
International audienceThis paper discusses some algorithmic issues when computing with a heterogeneo...
This paper discusses some algorithmic issues when computing with a heterogeneous network of workstat...
This paper presents a self-optimization methodology for parallel linear algebra rou-tines on heterog...
This paper describes the design and the implementation of parallel routines in the Heterogeneous Sca...
(eng) We study the implementation of dense linear algebra computations, such as matrix multiplicatio...
We present a new approach to utilizing all CPU cores and all GPUs on heterogeneous multicore and mul...
The current state and foreseeable future of high performance scientific computing (HPC) can be descr...
International audienceWe study the implementation of dense linear algebra computations, such as matr...
This paper presents a package, called Heterogeneous PBLAS (HeteroPBLAS), which is built on top of PB...
Abstract. The paper presents a tool that ports ScaLAPACK programs designed to run on massively paral...
(eng) In this paper, we address the issue of implementing matrix-matrix multiplication on heterogene...
The aim of data and task parallel scheduling for dense linear algebra kernels is to minimize the pro...