Work

TSN Cache: Exploiting Data Localities in Graph Computing Applications

Year: 2025

Type: article

Abstract: This article finds that the reusability of vertices in the same graph in graph processing differs, and the high-reuse and low-reuse vertices are stored together. These phenomena lead to the inability of existing GPU architectures to capture the reusability of graph processing. The most advanced cache optimization strategies cannot implement different management strategies for data with different reusability, which is an essential reason for graph processing’s poor performance. Therefore, we propose a TSN cache scheme for the GPU platform. This scheme employs distinct management strategies for data with varying reusability in the cache, effectively leveraging the locality of these different data types. In addition, the TSN cache scheme can also reduce the probability of cache thrashing caused by low-reuse data. This article evaluates multiple graph algorithms and datasets and shows that the TSN cache scheme achieves an average speedup of 1.38 compared with the baseline scheme.

Source: ACM Transactions on Architecture and Code Optimization

DOI: 10.1145/3721286

Authors: Chaoyang Jia, Jingyu Liu, Shi Chen, Kai Lu, Li Shen

Institution: National University of Defense Technology

Language: English

Topic: Graph Theory and Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: National Natural Science Foundation of China

Award: G3185965449