The Use of Empirical Studies in the Development of High End Computing Applications

Abstract

This report provides a description of the research and development activities towards learning much about the development and measurement of productivity in high performance computing environments. Many objectives were accomplished including the development of a methodology for measuring productivity in the parallel programming domain. This methodology was tested over 25 times at 8 universities across the United States and can be used to aid other researchers studying similar environments. The productivity measurement methodology incorporates both development time and performance into a single productivity number. An Experiment Manager tool for collecting data on the development of parallel programs, as well as a suite of tools to aid in the capture and analysis of such data was also developed. Lastly, several large scale development environments were studied in order to better understand the environment used to build large parallel programming applications. That work also included several surveys and interviews with many professional programmers in these environments.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2009
Accession Number
ADA511351

Entities

People

  • Marvin V. Zelowitz
  • Victor Basili

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Energy and Power Technologies
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Climate Change
  • Computational Fluid Dynamics
  • Computational Science
  • Computer Programming
  • Computer Programs
  • Computer Science
  • Computers
  • Control Systems
  • Databases
  • Debugging
  • Graphical User Interface
  • High Performance Computing
  • Operating Systems
  • Organizational Structure
  • Software Development
  • Software Metrics
  • Software Testing

Fields of Study

  • Computer science

Readers

  • Instructional Design and Training Evaluation.
  • Organizational Process Management (OPM).
  • Parallel and Distributed Computing.