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Hybrid Genetic Algorithm for Allocation Mapping in Processor Array Design

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Large-Scale Scientific Computing (LSSC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8353))

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Abstract

In this paper a hybrid genetic method for processor arrays design dedicated to realization of linear algebra algorithm for banded matrices is presented. The proposed method is a modification of previous genetic algorithm which is characterized by few important advantages relative to well-known linear projection methods. The main disadvantages of this previous method are: long program runtime and problems with obtaining acceptable allocation mapping results for huge information dependency graphs. Linear projection methods don’t allow obtaining better allocation mapping solutions but are characterized by shorter program runtime. New hybrid algorithm combines these both linear and genetic methods and merges their advantages. Summarizing, this new proposed method is characterized by: a shorter program runtime, better allocation mapping results in comparison with both previous methods, possibly allocation mapping for large input linear algebra banded matrices and possibility of defining the designed processor array structure.

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Authors and Affiliations

  1. Koszalin University of Technology, ul. Śniadeckich 2, 75-453, Koszalin, Poland

    Piotr Ratuszniak

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  1. Piotr Ratuszniak
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Correspondence to Piotr Ratuszniak .

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Editors and Affiliations

  1. Inst. of Info. & Comm. Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Ivan Lirkov

  2. Bulgarian Academy of Sciences, Sofia, Bulgaria

    Svetozar Margenov

  3. Dept. of Informatics and Mathematical Modell, Technical University of Denmark, Kongens Lyngby, Denmark

    Jerzy Waśniewski

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Ratuszniak, P. (2014). Hybrid Genetic Algorithm for Allocation Mapping in Processor Array Design. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2013. Lecture Notes in Computer Science(), vol 8353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43880-0_34

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  • DOI: https://doi.org/10.1007/978-3-662-43880-0_34

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43879-4

  • Online ISBN: 978-3-662-43880-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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