Recent application studies have shown that many irregular applications have a generalized data parallelism that manifests itself as iterative computations over worklists of different kinds. In general, there are complex dependencies between iterations. These dependencies cannot be elucidated statically because they depend on the inputs to the program; thus, optimistic parallel execution is the only tractable approach to parallelizing these applications. We have built a system called Galois that supports this style of parallel execution. Its main features are (i) set iterators for expressing worklist-based data parallelism, and (ii) a runtime system that performs optimistic parallelization of these iterators, detecting conflicts and rolling back computations as needed. Our work builds on the Galois system, and it addresses the problem of scheduling iterations of set iterators on multiple cores. The policy used by the base Galois system is to assign an iteration to a core whenever it needs work to do, but we show in this paper that this policy is not optimal for many applications. We also argue that OpenMP-style DO-ALL loop scheduling directives such as chunked and guided self-scheduling are too simplistic for irregular programs. These difficulties led us to develop a general scheduling framework for irregular problems; OpenMP-style scheduling strategies are special cases of this general approach. We also provide hooks into our framework, allowing the programmer to leverage application knowledge to further tune a schedule for a particular application. To evaluate this framework, we implemented it as an extension of the Galois system. We then tested the system using five real-world, irregular, data-parallel applications. Our results show that (i) the optimal scheduling policy can be different for different applications and often leverages application-specific knowledge and (ii) implementing these schedules in the Galois system is relatively straightforward.Copyright 2008 ACM.
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