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Load balancing in homogeneous pipeline based applications

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Title: Load balancing in homogeneous pipeline based applications
Authors: Moreno, A.1 amoreno@euss.cat, Cesar, E.2 eduardo.cesar@uab.cat, Guevara, A.2 alex.guevara@caos.uab.es, Sorribes, J.2 joan.sorribes@uab.cat, Margalef, T.2 tomas.margalef@uab.cat
Source: Parallel Computing. Mar2012, Vol. 38 Issue 3, p125-139. 15p.
Subjects: Pipeline computers, Parallel computer software, Computer programming, Search algorithms, Computational complexity, Parallel logic programming
Abstract: Abstract: We propose to use knowledge about a parallel application’s structure that was acquired with the use of a skeleton based development strategy to dynamically improve its performance. Parallel/distributed programming provides the possibility of solving highly demanding computational problems. However, this type of application requires support tools in all phases of the development cycle because the implementation is extremely difficult, especially for non-expert programmers. This work shows a new strategy for dynamically improving the performance of pipeline applications. We call this approach Dynamic Pipeline Mapping (DPM), and the key idea is to have free computational resources by gathering the pipeline’s fastest stages and then using these resources to replicate the slowest stages. We present two versions of this strategy, both with complexity O(N log (N)) on the number of pipe stages, and we compare them to an optimal mapping algorithm and to the Binary Search Closest (BSC) algorithm . Our results show that the DPM leads to significant performance improvements, increasing the application throughput up to 40% on average. [Copyright &y& Elsevier]
Copyright of Parallel Computing is the property of Elsevier B.V. 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.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: <searchLink fieldCode="DE" term="%22Pipeline+computers%22">Pipeline computers</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+computer+software%22">Parallel computer software</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+programming%22">Computer programming</searchLink><br /><searchLink fieldCode="DE" term="%22Search+algorithms%22">Search algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+complexity%22">Computational complexity</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+logic+programming%22">Parallel logic programming</searchLink>
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  Data: Abstract: We propose to use knowledge about a parallel application’s structure that was acquired with the use of a skeleton based development strategy to dynamically improve its performance. Parallel/distributed programming provides the possibility of solving highly demanding computational problems. However, this type of application requires support tools in all phases of the development cycle because the implementation is extremely difficult, especially for non-expert programmers. This work shows a new strategy for dynamically improving the performance of pipeline applications. We call this approach Dynamic Pipeline Mapping (DPM), and the key idea is to have free computational resources by gathering the pipeline’s fastest stages and then using these resources to replicate the slowest stages. We present two versions of this strategy, both with complexity O(N log (N)) on the number of pipe stages, and we compare them to an optimal mapping algorithm and to the Binary Search Closest (BSC) algorithm . Our results show that the DPM leads to significant performance improvements, increasing the application throughput up to 40% on average. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Parallel Computing is the property of Elsevier B.V. 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=70386052
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