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Many parallel algorithms are naturally expressed at a fine level of granularity, often finer than a MIMD parallel system can exploit efficiently. Most builders of parallel systems have looked to either the programmer or a parallelizing compiler to increase the granularity of such algorithms. In this paper we explore a third approach to the granularity problem by analyzing two strategies for combining parallel tasks dynamically at run-time. We reject the simpler load-based inlining method, where tasks are combined based on dynamic load level, in favor of the safer and more robust lazy task creation method, where tasks are created only retroactively as processing resources become available.
Mohr et al. (Tue,) studied this question.
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