Shaizeen Aga
ABSTRACT
Recursive parallel programming models such as Cilk strive to simplify the task of parallel programming by enabling a simple divide-and-conquer programming model. This model is effective in recursively partitioning work into smaller parts and combining their results. However, recursive work partitioning can impose additional constraints on concurrency than is implied by the true dependencies in a program. In this paper, we present a speculation-based approach to alleviate the concurrency constraints imposed by such recursive parallel programs. We design a runtime infrastructure that supports speculative execution and a predictor to accurately learn and identify opportunities to relax extraneous concurrency constraints. Experimental evaluation demonstrates that speculative relaxation of concurrency constraints can deliver gains of up to 1.6x on 30 cores over baseline Cilk.
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Index Terms
- CilkSpec: optimistic concurrency for Cilk
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