A High-Quality and Fast Maximal Independent Set Implementation for GPUs

Abstract

Computing a maximal independent set is an important step in many parallel graph algorithms. This article introduces ECL-MIS, a maximal independent set implementation that works well on GPUs. It includes key optimizations to speed up computation, reduce the memory footprint, and increase the set size. Its CUDA implementation requires fewer than 30 kernel statements, runs asynchronously, and produces a deterministic result. It outperforms the maximal independent set implementations of Pannotia, CUSP, and IrGL on each of the 16 tested graphs of various types and sizes. On a Titan X GPU, ECL-MIS is between 3.9 and 100 times faster (11.5 times, on average). ECL-MIS running on the GPU is also faster than the parallel CPU codes Ligra, Ligra+, and PBBS running on 20 Xeon cores, which it outperforms by 4.1 times, on average. At the same time, ECL-MIS produces maximal independent sets that are up to 52% larger (over 10%, on average) compared to these preexisting CPU and GPU implementations. Whereas these codes produce maximal independent sets that are, on average, about 15% smaller than the largest possible such sets, ECL-MIS sets are less than 6% smaller than the maximum independent sets.