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 programs: data partitioning, data-centric work assignment, lock coarsening, and over-decomposition. These mechanisms can be used to exploit locality of reference, reduce mis-speculation, and lower synchronization overhead. We also argue that the design of the Galois system permits these mechanisms to be used with relatively little modification to the user code. Finally, we present experimental results that demonstrate the utility of these mechanisms.
optimistic parallelism data partitioning lock coarsening locality languages irregular programs over-decomposition