Data-intensive applications dominated by random accesses to large working sets fail to utilize the computing power of modern processors. Graph random walk, an indispensable workhorse for many important graph processing and learning applications, is one prominent case of such applications. Existing graph random walk systems are currently unable to match the GPU-side node embedding training speed.
This work reveals that existing approaches fail to effectively utilize the modern CPU memory hierarchy, due to the widely held assumption that the inherent randomness in random walks and the skewed nature of graphs render most memory accesses random. We demonstrate that there is actually plenty of spatial and temporal locality to harvest, by careful partitioning, rearranging, and batching of operations. The resulting system, FlashMob, improves both cache and memory bandwidth utilization by making memory accesses more sequential and regular. We also found that a classical combinatorial optimization problem (and its exact pseudo-polynomial solution) can be applied to complex decision making, for accurate yet efficient data/task partitioning. Our comprehensive experiments over diverse graphs show that our system achieves an order of magnitude performance improvement over the fastest existing system. It processes a 58GB real graph at higher per-step speed than the existing system on a 600KB toy graph fitting in the L2 cache.