Enhancing High-Throughput GPU Random Walks Through Multi-Task Concurrency Orchestration

Random walk is a powerful tool for large-scale graph learning, but its high computational demand presents a challenge. While GPUs can accelerate random walk tasks, current frameworks fail to fully utilize GPU parallelism due to memory-to-compute bandwidth imbalance. In this paper, CoWalker, an efficient GPU framework, is proposed to facilitate concurrent execution of random walks for high overall throughput. CoWalker features three novel designs. First, it incorporates a multi-level execution model that effectively orchestrates diverse walk tasks and reduces GPU stalls based on multiple graph characteristics. Second, it collaboratively manages graph data and streaming multiprocessors to minimize memory access interference and maximize core utilization under concurrent tasks. Finally, a multi-dimensional scheduler selects compatible random walk task combinations based on memory footprints to achieve maximum throughput. CoWalker significantly improves throughput over state-of-the-art baselines by mitigating concurrency overheads and effectively harnessing GPU parallelism. Our extensive evaluations on real-world workloads demonstrate that CoWalker achieves 2.75 × higher overall system throughput compared with commercial tools and 1.56 × over the SOTA academic system.