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Task mapping and scheduling are two very difficult problems that must be addressed when a sequential program is transformed into a parallel program. Since these problems are NP‐hard, compiler writers have opted to concentrate their efforts on optimizations that produce immediate gains in performance. As a result, current parallelizing compilers either use very simple methods to deal with task scheduling or they simply ignore it altogether. Unfortunately, the programmer does not have this luxury. The burden of repartitioning or rescheduling, should the compiler produce inefficient parallel code, lies entirely with the programmer. We were able to create an algorithm (called a metaheuristic), which automatically chooses a scheduling heuristic for each input program. The metaheuristic produces better schedules in general than the heuristics upon which it is based. This technique was tested on a suite of real scientific programs written in SISAL and simulated on four different network configurations. Averaged over all of the test cases, the metaheuristic out‐performed all eight underlying scheduling algorithms; beating the best one by 2%, 12%, 13%, and 3% on the four separate network configurations. It is able to do this, not always by picking the best heuristic, but rather by avoiding the heuristics when they would produce very poor schedules. For example, while the metaheuristic only picked the best algorithm about 50% of the time for the 100 Gbps Ethernet, its worst decision was only 49% away from optimal. In contrast, the best of the eight scheduling algorithms was optimal 30% of the time, but its worst decision was 844% away from optimal

Automatic Choice of Scheduling Heuristics for Parallel/Distributed Computing