Mixed speculative multithreaded execution models
ACM Transactions on Architecture and Code Optimization (TACO)
Turning nondeterminism into parallelism
Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications
Automatic Skeleton-Driven Memory Affinity for Transactional Worklist Applications
International Journal of Parallel Programming
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In this paper we work on the parallelization of the inherently serial Dijkstra's algorithm on modern multicore platforms. Dijkstra's algorithm is a greedy algorithm that computes Single Source Shortest Paths for graphs with non-negative edges and is based on the iterative extraction of nodes from a priority queue. This property limits the explicit parallelism of the algorithm and any attempt to utilize the remaining parallelism results in significant slowdowns due to synchronization overheads. To deal with these problems, we employ the concept of Helper Threads (HT) to extract parallelism on a non-traditional fashion and Transactional Memory (TM) to efficiently orchestrate the concurrent threads' accesses to shared data structures. Results demonstrate that the proposed implementation is able to achieve performance speedups (reaching up to 1.84 for 14 threads), indicating that the two paradigms could be efficiently combined.