Related papers: A grid environment consisting of heterogeneous com…

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Hardware heterogeneity is here to stay for high-performance computing. Large-scale systems are currently equipped with multiple GPU accelerators per compute node and are expected to incorporate more specialized hardware in the future. This…

This paper has been withdrawn by the author, since the author does not have enough time to answer every questions on this result.

The challenge to fully exploit the potential of existing and upcoming scientific instruments like large single-dish radio telescopes is to process the collected massive data effectively and efficiently. As a "quasi 2D stencil computation"…

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This paper has been withdrawn due to its publication

With the rapid development in wide area networks and low cost, powerful computational resources, grid computing has gained its popularity. With the advent of grid computing, space limitations of conventional distributed systems can be…

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The paper is being withdrawn since the results are incorporated in paper arxiv.org/abs/math.AG/0306195.

This paper has been withdrawn by the author because overcame by arXiv:0910.4694

In the past decade, high performance compute capabilities exhibited by heterogeneous GPGPU platforms have led to the popularity of data parallel programming languages such as CUDA and OpenCL. Such languages, however, involve a steep…

This paper has been withdrawn by the author. This paper is now obsolete. For a solution please see: arXiv:/1205.4265.

The growing demand for computational resources in machine learning has made efficient resource allocation a critical challenge, especially in heterogeneous hardware clusters where devices vary in capability, age, and energy efficiency.…

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This paper has been withdrawn due to an incorrect proof.

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This paper has been withdrawn by the authors. It has been superseded by hep-th/0309154