HAL: a framework for the automated analysis and design of high-performance algorithms

  • Authors:

  • Christopher Nell;Chris Fawcett;Holger H. Hoos;Kevin Leyton-Brown

  • Affiliations:

  • University of British Columbia, Vancouver, BC, Canada;University of British Columbia, Vancouver, BC, Canada;University of British Columbia, Vancouver, BC, Canada;University of British Columbia, Vancouver, BC, Canada

  • Venue:
  • LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
  • Year:
  • 2011

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Abstract

Sophisticated empirical methods drive the development of high-performance solvers for an increasing range of problems from industry and academia. However, automated tools implementing these methods are often difficult to develop and to use. We address this issue with two contributions. First, we develop a formal description of meta-algorithmic problems and use it as the basis for an automated algorithm analysis and design framework called the High-performance Algorithm Laboratory. Second, we describe HAL 1.0, an implementation of the core components of this framework that provides support for distributed execution, remote monitoring, data management, and analysis of results. We demonstrate our approach by using HAL 1.0 to conduct a sequence of increasingly complex analysis and design tasks on state-of-the-art solvers for SAT and mixed-integer programming problems.