AridWalk: Efficient Graph Random Walks on a Resource-Limited Computational Storage Device
Journal
Proceedings of the International Symposium on Low Power Electronics and Design
Start Page
1
End Page
7
ISSN
15334678
ISBN (of the container)
979-833152710-5
ISBN
[9798331527105]
Date Issued
2025-12-03
Author(s)
Abstract
The effective utilization of graph structures relies on obtaining high-quality graph embeddings. Traditional embedding algorithms, such as DeepWalk and Node2Vec, which rely on random walk sampling, encounter significant challenges when applied to large-scale graphs due to the substantial data transfer demands between storage and memory. To address these limitations, we propose AridWalk, which enables a Computational Storage Device (CSD) to perform random walks directly at the storage level, minimizing external data transfers by only transferring essential data. To address the constraints of limited computational resources in the CSD, AridWalk is designed to maximize DRAM utilization while significantly reducing internal data movements, specifically between internal DRAM and flash memory. Experimental results demonstrate that AridWalk substantially decreases internal data movement, providing an efficient and scalable solution for conducting in-storage random walks on large graphs.
Event(s)
30th IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2025
Subjects
computational storage device
in-storage computing
random walk
Publisher
IEEE
Type
conference paper