Abstract
Loops are a large source of parallelism for many numerical applications. An important issue in the parallel execution of loops is how to schedule them so that the workload is well balanced among the processors. Most existing loop scheduling algorithms were designed for shared-memory multiprocessors, with uniform memory access costs. These approaches are not suitable for distributed-memory multiprocessors where data locality is a major concern and communication costs are high. This paper presents a new scheduling algorithm in which data locality is taken into account. Our approach combines both worlds, static and dynamic scheduling, in a two-level (overlapped) fashion. This way data locality is considered and communication costs are limited. The performance of the new algorithm is evaluated on a CM-5 message-passing distributed-memory multiprocessor.
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