Wasp: Efficient Asynchronous Single-Source Shortest Path on Multicore Systems via Work Stealing

Paper in proceeding, 2025

The Single-Source Shortest Path (SSSP) problem is a fundamental graph problem with an extensive set of real-world applications. State-of-the-art parallel algorithms for SSSP, such as the Δ-stepping algorithm, create parallelism through priority coarsening. Priority coarsening results in redundant computations that diminish the benefits of parallelization and limit parallel scalability. This paper introduces Wasp, a novel SSSP algorithm that reduces parallelism-induced redundant work by utilizing asynchrony and an efficient priority-aware work stealing scheme. Contrary to previous work, Wasp introduces redundant computations only when threads have no high-priority work locally available to execute. This is achieved by a novel priority-aware work stealing mechanism that controls the inefficiencies of indiscriminate priority coarsening. Experimental evaluation shows competitive or better performance compared to GAP, GBBS, MultiQueues, Galois, Δ^∗-stepping, and ρ-stepping on 13 diverse graphs with geometric mean speedups of 2.23× on AMD Zen 3 and 2.16× on Intel Sapphire Rapids using 128 threads.