Liwei Si
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Liwei Si has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Hai Jin
Huazhong University of Science and Technology, Wuhan, China
1 shared publication - Huanghai Liu
National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology
1 shared publication - Huize Li
University of Central Florida, Orlando, --, USA
1 shared publication - Jingling Xue
University of New South Wales
1 shared publication - Long Zheng
HuaZhong University of Science and Technology, Wuhan, Hubei, China
1 shared publication - Qinggang Wang
HuaZhong University of Science and Technology, Wuhan, Hubei, China
1 shared publication
Latest Publications
- Open Access
- 22 March 2026
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Architecture and Code Optimization
- Vol. 23 (1) , 1-26
Accelerating Out-of-Core Graph Random Walk Processing via Locality-Aware Algorithm-Hardware Co-Design
Modern out-of-core random walk systems partition large disk-resident graphs into blocks and use walkers to efficiently scale graph processing. However, our study reveals a critical inefficiency: while most walker updates are processed efficiently, a small fraction of remaining walkers incur disproportionately high block I/Os, accounting for over 75% of total block I/O time. This performance bottleneck stems from static in-memory block layouts, which lead to excessive loading of irrelevant data during late-stage walker updates. To address this, we introduce LocWalker, a storage system that combines algorithmic and hardware innovations to optimize I/O efficiency for out-of-core random walks. At the algorithmic level, LocWalker specifically optimizes the last remaining few walkers processing through a skeleton-based walker updating scheme and a bounded breadth-first search (BBFS)-based block shrinking mechanism, both leveraging temporal and spatial locality to reduce the number and size of block I/Os per iteration during the iterative random walk process. Architecturally, LocWalker integrates a hardware-accelerated block provision engine on a computational SSD to minimize additional computational overhead introduced by our innovative features. High-level APIs enable easy integration with existing systems. Evaluated against SOWalker, GraphWalker, and NosWalker with a Samsung SmartSSD, LocWalker achieved substantial I/O time reductions (1.82× to 11.06×) and overall performance improvements (1.44× to 5.43×).
- 22 March 2026
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Architecture and Code Optimization
- Vol. 23 (1) , 1-26
Open AccessAccelerating Out-of-Core Graph Random Walk Processing via Locality-Aware Algorithm-Hardware Co-Design
Modern out-of-core random walk systems partition large disk-resident graphs into blocks and use walkers to efficiently scale graph processing. However, our study reveals a critical inefficiency: while most walker updates are processed efficiently, a small fraction of remaining walkers incur disproportionately high block I/Os, accounting for over 75% of total block I/O time. This performance bottleneck stems from static in-memory block layouts, which lead to excessive loading of irrelevant data during late-stage walker updates. To address this, we introduce LocWalker, a storage system that combines algorithmic and hardware innovations to optimize I/O efficiency for out-of-core random walks. At the algorithmic level, LocWalker specifically optimizes the last remaining few walkers processing through a skeleton-based walker updating scheme and a bounded breadth-first search (BBFS)-based block shrinking mechanism, both leveraging temporal and spatial locality to reduce the number and size of block I/Os per iteration during the iterative random walk process. Architecturally, LocWalker integrates a hardware-accelerated block provision engine on a computational SSD to minimize additional computational overhead introduced by our innovative features. High-level APIs enable easy integration with existing systems. Evaluated against SOWalker, GraphWalker, and NosWalker with a Samsung SmartSSD, LocWalker achieved substantial I/O time reductions (1.82× to 11.06×) and overall performance improvements (1.44× to 5.43×).