Graph Computation with Adaptive Granularity

Ruiqi Xu, Yue Wang, Xiaokui Xiao

Abstract: Despite the development of various distributed graph systems, little attention has been paid to the granularity of computation and communication, which can significantly impact overall efficiency. Moreover, users often struggle to write and optimize new parallel algorithms to fit different programming abstractions, which can be a daunting task. To address these challenges, this paper introduces Argan, a parallel graph system that offers efficient adaptive-grained executions and a user-friendly abstraction. Argan utilizes the adaptive-Grained Asynchronous Parallel (GAP) model, which enables runtime adjustments of granularity to enhance performance. Additionally, its programming model allows users to directly derive parallel programs from existing batch sequential algorithms. Our experiments using real-life and synthetic graphs demonstrate that for a variety of graph applications, GAP effectively improves the performance of Argan, which outperforms Grap +, PowerSwitch, and Maiter.