HAL: A Framework for the Automated Analysis and Design of High-Performance Algorithms
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.
KeywordsDesign Procedure Automate Analysis Mixed Integer Programming Benchmark Instance Target Problem
Unable to display preview. Download preview PDF.
- 2.Chiarandini, M., Fawcett, C., Hoos, H.H.: A modular multiphase heuristic solver for post enrollment course timetabling (extended abstract). In: PATAT (2008)Google Scholar
- 4.Hoos, H.H.: Computer-aided design of high-performance algorithms. Technical Report TR-2008-16, University of British Columbia, Computer Science (2008)Google Scholar
- 6.Hutter, F., Hoos, H.H., Stützle, T.: Automatic algorithm configuration based on local search. In: AAAI (2007)Google Scholar
- 10.Xu, L., Hoos, H.H., Leyton-Brown, K.: Hydra: Automatically configuring algorithms for portfolio-based selection. In: AAAI (2010)Google Scholar
- 12.Balint, A., Gall, D., Kapler, G., Retz, R.: Experiment design and administration for computer clusters for SAT-solvers (EDACC). JSAT 7, 77–82 (2010)Google Scholar
- 14.Kadioglu, S., Malitsky, Y., Sellmann, M., Tierney, K.: ISAC – Instance-specific algorithm configuration. In: ECAI (2010)Google Scholar
- 15.Babić, D.: Exploiting Structure for Scalable Software Verification. PhD thesis, University of British Columbia, Vancouver, Canada (2008)Google Scholar
- 16.Hutter, F., Babić, D., Hoos, H.H., Hu, A.: Boosting verification by automatic tuning of decision procedures. In: FMCAD (2007)Google Scholar
- 17.Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration (extended version). Technical Report TR-2010-10, University of British Columbia, Computer Science (2010)Google Scholar