Reducing Redundant Search in Parallel Graph Mining Using Exceptions

This paper proposes an implementation of a parallel graph mining algorithm using a task-parallel language having exception handling features. The performance of a straightforward task-parallel implementation is poor for many practical backtrack search algorithms due to pruning employed in them, a worker may prune a useless subtree, which is pruned before traversal in the sequential search algorithms for search space reduction, after another worker starts the traversal of it resulting in a large amount of redundant search. Such redundancy will be significantly reduced by letting a worker know that the subtree which it is traversing is pruned so that it aborts the traversal. This abortion can be implemented elegantly and efficiently using the task-parallel language that has a mechanism for exception handling by which all running parallel tasks in a try block with an exception are automatically aborted. We applied this abort mechanism to the graph mining algorithm called COPINE, which is practically used for drug discovery, using the task-parallel language Tascell. As a result, we reduced the search space by 31.9% and the execution time by 27.4% in a 28-worker execution.