Random Walks on Huge Graphs at Cache Efficiency
Ke Yang, Xiaosong Ma, Saravanan Thirumuruganathan, Kang Chen, Yongwei Wu
Abstract
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.
Topics & Concepts
Computer scienceRandom walkEmbeddingTheoretical computer scienceRandom graphParallel computingCacheGraphDistributed computingArtificial intelligenceMathematicsStatisticsAdvanced Graph Neural NetworksGraph Theory and AlgorithmsComplexity and Algorithms in Graphs