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Related Experiment Videos

Genetic Parallel Programming: design and implementation.

Sin Man Cheang1, Kwong Sak Leung, Kin Hong Lee

  • 1Department of Computing, Hong Kong Institute of Vocational Education (Kwai Chung Campus), 20 Hing Shing Road, Kwai Chung, Hong Kong. smcheang@vtc.edu.hk

Evolutionary Computation
|July 13, 2006
PubMed
Summary
This summary is machine-generated.

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This study introduces Genetic Parallel Programming (GPP) to evolve parallel programs for Multi-Arithmetic-Logic-Unit Processors (MAPs). GPP simplifies parallel programming and accelerates evolutionary computation, outperforming sequential approaches.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Parallel Computing

Background:

  • Writing parallel programs is complex for human developers.
  • High-speed evaluation of genetic programs requires specialized hardware like Multi-Arithmetic-Logic-Unit Processors (MAPs).
  • Existing methods may not efficiently evolve parallel programs.

Purpose of the Study:

  • To present a novel Genetic Parallel Programming (GPP) paradigm.
  • To evolve parallel programs for a Multi-Arithmetic-Logic-Unit Processor (MAP).
  • To demonstrate GPP's effectiveness and efficiency in evolving parallel programs.

Main Methods:

  • Developed a Genetic Parallel Programming (GPP) paradigm.
  • Utilized a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP) architecture for high-speed genetic program evaluation.

Related Experiment Videos

  • Tested GPP on 14 benchmark problems, comparing parallel and sequential evolution efforts.

    • Main Results:

    • GPP successfully evolves parallel programs.
    • Experimental results show GPP requires less computational effort than sequential programming counterparts.
    • GPP substantially speeds up the evolutionary process.

    Conclusions:

    • GPP offers a new approach for evolving feasible solutions in parallel program form.
    • The paradigm can serialize parallel programs into sequential ones if needed.
    • GPP is an effective and efficient method for accelerating evolutionary computation on parallel architectures.