FlashWalker: An In-Storage Accelerator for Graph Random Walks
Document Type
Conference Proceeding
Publication Date
7-15-2022
Department
Department of Computer Science
Abstract
Graph random walk is widely used in the graph processing as it is a fundamental component in graph analysis, ranging from vertices ranking to the graph embedding. Different from traditional graph processing workload, random walk features massive processing parallelisms and poor graph data reuse, being limited by low I/O efficiency. Prior designs for random walk mitigate slow I/O operations. However, the state-of-the-art random walk processing systems are bounded by slow disk I/O bandwidth, which is confirmed by our experiments with real-world graphs. To address this issue, we propose FlashWalker, an in-storage accelerator for random walk that moves walk updating close to graph data stored in flash memory, by exploiting significant parallelisms inside SSD. Featuring a heterogeneous and parallel processing system, FlashWalker includes a board-level accelerator, channel-level accelerators, and chip-level accelerators. To address challenges posed by the tight resource constraints for processing large-scale graphs, we propose novel designs: storing a few popular subgraphs in accelerators, the pre-walking for dense walks, two optimizations to search the subgraph mapping table, and a subgraph scheduling algorithm. We implement FlashWalker in RTL, showing small circuit area overhead. Our evaluation shows FlashWalker reduces the execution time of random walk algorithms by up to 660.50×, compared with GraphWalker, which is the state-of-the-art system for random walk algorithms.
Publication Title
Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022
ISBN
9781665481069
Recommended Citation
Niu, F.,
Yue, J.,
Shen, J.,
Liao, X.,
Liu, H.,
&
Jin, H.
(2022).
FlashWalker: An In-Storage Accelerator for Graph Random Walks.
Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1063-1073.
http://doi.org/10.1109/IPDPS53621.2022.00107
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16341