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Tool support for software lookup table optimization.

Chris Wilcox1, Michelle Mills Strout1, James M Bieman1

  • 1Computer Science Department, Colorado State University, 1873 Campus Delivery, Fort Collins, CO 80523, USA Tel.: +1 970 491 5792;

Scientific Programming
|February 18, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces Mesa, a tool automating lookup table (LUT) optimization for scientific applications. Mesa improves performance by reusing computed function results, offering better accuracy control and reducing manual effort in fuzzy reuse.

Keywords:
Lookup tablecode generationerror analysisfuzzy reusememoizationperformance optimizationscientific computing

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Area of Science:

  • Computer Science
  • Computational Science
  • Software Engineering

Background:

  • Scientific applications often face performance bottlenecks due to repeated calls to elementary functions.
  • Lookup Table (LUT) optimization offers a method to accelerate these functions by reusing prior computations, enabling 'fuzzy reuse' where approximations are acceptable.
  • Manual LUT optimization presents challenges in balancing performance and accuracy, requiring significant programming effort and experimentation.

Purpose of the Study:

  • To present a methodology and tool implementation for automating software LUT optimization.
  • To address the difficulties in controlling the performance-accuracy tradeoff inherent in manual LUT optimization.
  • To reduce the programming effort and potential for errors associated with manual LUT tuning.

Main Methods:

  • Developed the Mesa tool, which performs source-to-source transformations for C/C++ code.
  • Automated key aspects of LUT generation, including domain profiling, error analysis, and code generation.
  • Evaluated the Mesa tool across five diverse scientific applications.

Main Results:

  • Achieved significant performance improvements in various scientific domains.
  • Demonstrated performance gains of 3.0x-6.9x for molecular biology algorithms, 1.4x for molecular dynamics, 2.1x-2.8x for neural networks, and 4.6x for hydrology.
  • Showcased Mesa's ability to provide enhanced control over accuracy compared to manual methods.

Conclusions:

  • Mesa effectively automates software LUT optimization, simplifying a complex and error-prone process.
  • The tool enables developers to achieve substantial performance improvements with greater control over accuracy.
  • Mesa reduces the manual effort required for LUT optimization, making it more accessible for scientific applications.