Abstract—The paper presents a performance model that can be used to optimally distribute computations over heterogeneous computers. This model is application-centric representing the speed of each computer by a function of the problem size. This way it takes into account the processor heterogeneity, the heterogeneity of memory structure, and the memory limitations at each level of memory hierarchy. A problem of optimal partitioning of an n-element set over p heterogeneous processors using this performance model is formulated, and its efficient solution of the complexity O(p3×log2n) is given. Index Terms—Heterogeneous (hybrid) systems, Scheduling and task partitioning, Loa
The problem of partitioning systems of independent constrained-deadline sporadic tasks upon heteroge...
Abstract — Cluster computing is an innovative technology which broadens horizons in whole world busi...
There has been a recent increase of interest in heterogeneous computing systems, due partly to the f...
Abstract—The paper presents a performance model that can be used to optimally distribute computation...
In this paper, we address the problem of optimal distribu-tion of computational tasks on a network o...
With the variety of computer architectures available today, it often is difficult to determine which...
Abstract. High performance of data-parallel applications on heterogeneous platforms can be achieved ...
Abstract. The paper presents a new data partitioning algorithm for parallel computing on heterogeneo...
Heterogeneous platforms are mixes of different processing units in a compute node (e.g., CPUs+GPUs, ...
International audienceThe aim of the paper is to introduce general techniques in order to optimize t...
Abstract. In this paper, we present a novel algorithm of optimal matrix partitioning for parallel de...
The current state and foreseeable future of high performance scientific computing (HPC) can be descr...
The focus of this invited keynote paper (to be presented by H. J. Siegel) is mixed-machine heterogen...
The rapid progress of microprocessor and communication technologies has made the distributed computi...
Due to the character of the original source materials and the nature of batch digitization, quality ...
The problem of partitioning systems of independent constrained-deadline sporadic tasks upon heteroge...
Abstract — Cluster computing is an innovative technology which broadens horizons in whole world busi...
There has been a recent increase of interest in heterogeneous computing systems, due partly to the f...
Abstract—The paper presents a performance model that can be used to optimally distribute computation...
In this paper, we address the problem of optimal distribu-tion of computational tasks on a network o...
With the variety of computer architectures available today, it often is difficult to determine which...
Abstract. High performance of data-parallel applications on heterogeneous platforms can be achieved ...
Abstract. The paper presents a new data partitioning algorithm for parallel computing on heterogeneo...
Heterogeneous platforms are mixes of different processing units in a compute node (e.g., CPUs+GPUs, ...
International audienceThe aim of the paper is to introduce general techniques in order to optimize t...
Abstract. In this paper, we present a novel algorithm of optimal matrix partitioning for parallel de...
The current state and foreseeable future of high performance scientific computing (HPC) can be descr...
The focus of this invited keynote paper (to be presented by H. J. Siegel) is mixed-machine heterogen...
The rapid progress of microprocessor and communication technologies has made the distributed computi...
Due to the character of the original source materials and the nature of batch digitization, quality ...
The problem of partitioning systems of independent constrained-deadline sporadic tasks upon heteroge...
Abstract — Cluster computing is an innovative technology which broadens horizons in whole world busi...
There has been a recent increase of interest in heterogeneous computing systems, due partly to the f...