Heuropa ? Heuristic Optimization of Parallel Computations

The performance of almost all parallel algorithms and systems can be improved by the use of heuristics that a ect the parallel execution. However, since optimal guidance usually depends on many di erent in uences, establishing such heuristics is often di cult. Due to the importance of heuristics for optimizing parallel execution, and the similarity of the problems that arise for establishing such heuristics, the HEUROPA activity was founded to attack these problems in a uniform way. To overcome the di culties of specifying heuristics by hand, machine learning techniques have been employed to obtain heuristics automatically. This paper presents the general approach used for learning heuristics, describes the applications arising in the various subprojects, and provides a detailed case study using the approach for a particular application.