Embedded Genetic Allocator.

Abstract

This final report documents the work accomplished under the Embedded Genetic Allocator Program, a two year effort funded by DARPA/ITO. The major accomplishment of the program was to develop a new approach to the problem of automatically optimizing the use of memory and processor resources in high performance computing systems consisting of heterogeneous processor nodes connected on a high-speed interconnection fabric. This is frequently known as the mapping problem. The Embedded Genetic Allocation technology developed under this program can provide an automated mapping tool for the design and re-hosting of these systems. The approach developed is automatic and broadly applicable to a wide variety of system architectures. It consists of a hybrid genetic algorithm optimizer (the Embedded Genetic Allocator or EGA), coupled with a software performance monitoring system (various ones can be used). The results presented in this report demonstrate that the EGA can be used to optimize the allocation mappings real-world software, and that the resulting optimizations can rival or even improve upon those generated manually by a skilled programmer.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2000
Accession Number
ADA373451

Entities

People

  • David B. Cousins
  • Fred Roeber

Organizations

  • BBN Technologies

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Automatic
  • Coding
  • Computer Programming
  • Computer Programs
  • Computer Simulations
  • Computers
  • Detectors
  • Genetic Algorithms
  • High Performance Computing
  • Information Systems
  • Lessons Learned
  • Matched Filters
  • Operating Systems
  • Optimization
  • Software Design

Fields of Study

  • Computer science
  • Engineering

Readers

  • Computer Networking
  • Logistics and Supply Chain Management.
  • Software Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Biotechnology