C-SAW: A Framework for Graph Sampling and Random Walk on GPUs

Abstract

Many applications require to learn, mine, analyze and visualize large-scale graphs. These graphs are often too large to be addressed efficiently using conventional graph processing technologies. Fortunately, recent research efforts find out graph sampling and random walk, which significantly reduce the size of original graphs, can benefit the tasks of learning, mining, analyzing and visualizing large graphs by capturing the desirable graph properties. This paper introduces C-SAW, the first framework that accelerates Sampling and Random Walk framework on GPUs. Particularly, C-SAW makes three contributions: First, our framework provides a generic API which allows users to implement a wide range of sampling and random walk algorithms with ease. Second, offloading this framework on GPU, we introduce warp-centric parallel selection, and two novel optimizations for collision migration. Third, towards supporting graphs that exceed the GPU memory capacity, we introduce efficient data transfer optimizations for out-of-memory and multi-GPU sampling, such as workload-aware scheduling and batched multi-instance sampling. Taken together, our framework constantly outperforms the state of the art projects in addition to the capability of supporting a wide range of sampling and random walk algorithms.

Scalable Graph Neural Network Training: The Case for Sampling

“…Beyond offering an easy-to-use API, NextDoor introduced a novel approach to parallelize graph sampling called transit parallelism. This is better suited to GPU architectures than the approach used by other systems for graph sampling, such as KnightKing [37] and C-saw [30], and graph mining, such as Arabesque and others [3,7,11,17,18,28,33,34]. All these systems expand multiple samples in parallel and assign each sample to a group of consecutive threads, which could be part of the same warp.…”