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A Runtime Library for Platform-Independent Task Parallelism.

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Název: A Runtime Library for Platform-Independent Task Parallelism.
Autoři: Hadjidoukas, Panagiotis E., Lappas, Evaggelos, Dimakopoulos, Vassilios V.
Zdroj: 2012 20th Euromicro International Conference on Parallel, Distributed & Network-based Processing; 1/ 1/2012, p229-236, 8p
Abstrakt: With the increasing diversity of computing systems and the rapid performance improvement of commodity hardware, heterogeneous clusters become the dominant platform for low-cost, high-performance computing. Grid-enabled and heterogeneous implementations of MPI establish it as the de facto programming model for these environments. On the other hand, task parallelism provides a natural way for exploiting their hierarchical architecture. This hierarchy has been further extended with the advent of general-purpose GPU devices. In this paper we present the implementation of an MPI-based task library for heterogeneous and GPU clusters. The library offers an intuitive programming interface for multilevel task parallelism with transparent data management and load balancing. We discuss design and implementation issues regarding heterogeneity support and report performance results on heterogeneous cluster computing environments. [ABSTRACT FROM PUBLISHER]
Copyright of 2012 20th Euromicro International Conference on Parallel, Distributed & Network-based Processing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Complementary Index
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  Data: A Runtime Library for Platform-Independent Task Parallelism.
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  Data: <searchLink fieldCode="AR" term="%22Hadjidoukas%2C+Panagiotis+E%2E%22">Hadjidoukas, Panagiotis E.</searchLink><br /><searchLink fieldCode="AR" term="%22Lappas%2C+Evaggelos%22">Lappas, Evaggelos</searchLink><br /><searchLink fieldCode="AR" term="%22Dimakopoulos%2C+Vassilios+V%2E%22">Dimakopoulos, Vassilios V.</searchLink>
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  Data: 2012 20th Euromicro International Conference on Parallel, Distributed & Network-based Processing; 1/ 1/2012, p229-236, 8p
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: With the increasing diversity of computing systems and the rapid performance improvement of commodity hardware, heterogeneous clusters become the dominant platform for low-cost, high-performance computing. Grid-enabled and heterogeneous implementations of MPI establish it as the de facto programming model for these environments. On the other hand, task parallelism provides a natural way for exploiting their hierarchical architecture. This hierarchy has been further extended with the advent of general-purpose GPU devices. In this paper we present the implementation of an MPI-based task library for heterogeneous and GPU clusters. The library offers an intuitive programming interface for multilevel task parallelism with transparent data management and load balancing. We discuss design and implementation issues regarding heterogeneity support and report performance results on heterogeneous cluster computing environments. [ABSTRACT FROM PUBLISHER]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of 2012 20th Euromicro International Conference on Parallel, Distributed & Network-based Processing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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