Dynamic parallelism for simple and efficient GPU graph algorithms
Peter Zhang, Eric Holk, John Matty, Samantha Misurda, Marcin Zalewski, Jonathan Chu, Scott McMillan, Andrew Lumsdaine · 2015
Dynamic parallelism allows GPU kernels to launch additional kernels at runtime directly from the GPU. In this paper we show that dynamic parallelism enables relatively simple high-performance graph algorithms for GPUs. We present breadth-first search (BFS) and single-source shortest paths (SSSP) algorithms that use dynamic parallelism to adapt to the irregular and data-driven nature of these problems. Our approach results in simple code that closely follows the high-level description of the algorithms but yields performance competitive with the current state of the art.