Skyway: Accelerate Graph Applications with a Dual-Path Architecture and Fine-Grained Data Management
Authors: M. Zou, M. Zhang, R. Wang, X.-H. Sun, X. Ye, D. Fan, Z. Tang
Date: July, 2024
Venue: Journal of Computer Science and Technology (Volume 39)
Type: Journal
Abstract
Graph processing is a vital component of many AI and big data applications. However, due to its poor locali- ty and complex data access patterns, graph processing is also a known performance killer of AI and big data applications. In this work, we propose to enhance graph processing applications by leveraging fine-grained memory access patterns with a dual-path architecture on top of existing software-based graph optimizations. We first identify that memory accesses to the offset, edge, and state array have distinct locality and impact on performance. We then introduce the Skyway architec- ture, which consists of two primary components: 1) a dedicated direct data path between the core and memory to transfer state array elements efficiently, and 2) a data-type aware fine-grained memory-side row buffer hardware for both the new- ly designed direct data path and the regular memory hierarchy data path. The proposed Skyway architecture is able to im- prove the overall performance by reducing the memory access interference and improving data access efficiency with a minimal overhead. We evaluate Skyway on a set of diverse algorithms using large real-world graphs. On a simulated four- core system, Skyway improves the performance by 23% on average over the best-performing graph-specialized hardware optimizations.