Optimistic parallelism benefits from data partitioning

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Optimistic parallelism benefits from data partitioning
Recent studies of irregular applications such as finite-element mesh generators and data-clustering codes have shown that these applications have a generalized data parallelism arising from the use of iterative algorithms that perform computations on elements of worklists. In some irregular applications, the computations on different elements are independent. In other applications, there may be complex patterns of dependences between these computations. The Galois system was designed to exploit this kind of irregular data parallelism on multicore processors. Its main features are (i) two kinds of 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. Detection of conflicts and rolling back iterations requires information from class implementors. In this paper, we introduce mechanisms to improve the execution efficiency of Galois pro...
Milind Kulkarni, Keshav Pingali, Ganesh Ramanaraya
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Authors Milind Kulkarni, Keshav Pingali, Ganesh Ramanarayanan, Bruce Walter, Kavita Bala, L. Paul Chew
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