Random walk serves as a powerful tool in dealing with large-scale graphs, reducing data size while preserving structural information. Unfortunately, existing system frameworks all focus on the execution of a single walker task in serial. We propose CoWalker, a high-throughput GPU random walk framework tailored for concurrent random walk tasks. It introduces a multi-level concurrent execution model to allow concurrent random walk tasks to efficiently share GPU resources with low overhead. Our system prototype confirms that the proposed system could outperform (up to 54%) the state-of-the-art in a wide spectral of scenarios.

Tapping into secondary memory resources, i.e., far memory (FM), has shown huge potential to improve the cost-efficiency of data centers. Recent advances in both storage-based vertical FM and network-based horizontal FM have raised new questions about leveraging hybrid FM tiers to achieve the best performance per bit of memory. It is still unclear how to efficiently place tasks when far memory access is enabled.In this work, we propose HyFarM, a novel task management strategy for hybrid FM clusters. We analyze FM sensitivity and cooperatively co-locate tasks to enable high utilization and scalability. Further, by tapping into dynamic memory adaption within and across servers, our strategy allows one to consistently deliver high performance on memory-intensive tasks. We evaluate our design with a heavily instrumented testbench. Compared with the state-of-the-art designs, HyFarM respectively improves memory utilization and the overall performance per bit (PPB) by up to 17.6% and 20.5%, with minor overhead.