一句话：提出 NosWalker 解耦架构，将随机游走采样与游走处理分离，以提升外存大规模图上的游走吞吐。
外存随机游走是在大规模图上经济地运行数十亿游走者的重要手段，但现有系统多构建于通用外存图处理框架，未能利用随机游走应用的独有特性。随机游走的采样过程可与游走者处理解耦，因此系统只需在内存中保留预采样结果，而该结果通常远小于整个边集；同时，随机游走性能主要由每秒移动步数而非游走者数量决定，且独立游走者无需被同时处理。NosWalker 据此采用解耦架构，将采样与游走处理分离，并据此调度外存访问与计算。摘要未给出具体实验数据与结果。
Out-of-core random walk system has recently attracted a lot of attention as an economical way to run billions of walkers over large graphs. However, existing out-of-core random walk systems are all built upon general out-of-core graph processing frameworks, and hence do not take advantage of the unique properties of random walk applications. Different from traditional graph analysis algorithms, the sampling process of random walk can be decoupled from the processing of the walkers. It enables the system to reserve only pre-sample results in memory, which are typically much smaller than the entire edge set. Moreover, in random walk, it is not the number of walkers but the number of steps moved per second that dominates the overall performance. Thus, with independent walkers, there is no need to process all the walkers simultaneously.
@inproceedings{paperbot10068,
title = {NosWalker: A Decoupled Architecture for Out-of-Core Random Walk Processing},
author = {Shuke Wang and Mingxing Zhang and Ke Yang and Kang Chen and Shaonan Ma and Jinlei Jiang and Yongwei Wu},
booktitle = {ASPLOS},
year = {2023},
doi = {10.1145/3582016.3582025}
}