HAL: A Framework for the Automated Analysis and Design of High-Performance Algorithms

  • Christopher Nell
  • Chris Fawcett
  • Holger H. Hoos
  • Kevin Leyton-Brown
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6683)


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.


Design Procedure Automate Analysis Mixed Integer Programming Benchmark Instance Target Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Christopher Nell
    • 1
  • Chris Fawcett
    • 1
  • Holger H. Hoos
    • 1
  • Kevin Leyton-Brown
    • 1
  1. 1.University of British ColumbiaVancouverCanada

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