Work-Load Balancing in Highly Parallel Depth-First Search
نویسنده
چکیده
Among the various approaches for parallel depthrst search (DFS), the stack-splitting schemes are most popular. However, as shown in this paper, dynamical stack-splitting is not suitable for massively parallel systems with several hundred processors. Initial work-load imbalances and work packets of dissimilar sizes cause a high communication overhead. We compare work-load balancing strategies of two depthrst searches and propose a scheme that uses ne-grained xed-sized work packets. In its iterativedeepening variant (named AIDA*) the global workload distribution improves from one iteration to the next. As a consequence, the communication overhead decreases with increasing search time.
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