《Wonderland: A Novel Abstraction-Based Out-Of-Core Graph Processing System》章明星

在2018年3月28日于美国弗吉尼亚州威廉斯堡结束的ACM ASPLOS 2018会议上，计算机系高性能所师生发表了两篇长文。一篇是我系博士生章明星为第一作者，导师武永卫为通讯作者的“Wonderland: A Novel Abstraction-Based Out-Of-Core Graph Processing System”《Wonderland：一种新型的基于抽象的核外图处理系统》（作者：章明星、武永卫、Zhuo Youwei、钱学海、Huan Chenyin、陈康）；另一篇是我系博士生季宇为第一作者，导师张悠慧为通讯作者的“Bridging the Gap Between Neural Networks and Neuromorphic Hardware with A Neural Network Compiler”《以编译技术来弥补软件神经网络与神经形态硬件间的差距》（作者：季宇、张悠慧、陈文光、谢源）。这也是继去年ASPLOS 2017以后，高性能所连续第二年在该会议上发表两篇论文，是目前在该会议上发表论文最多的国内研究团队。本届ASPLOS会议共收到301篇投稿，录用56篇（录用率18.6%），反映了综合体系结构、编程语言和操作系统三个方向的计算机系统领域的最高水平。

近年来，在校、系学科建设项目支持下，计算机系高性能所E级高性能计算机研究团队对于新型计算机体系结构开展了深入研究，该两篇文章即分别针对两大类新型计算系统，图计算系统与类脑计算系统开展系统与核心软件方面的研究。

ASPLOS会议全称为ACM International Conference on Architectural Support for Programming Languages and Operating Systems，是综合体系结构、编程语言和操作系统三个方向的计算机系统领域顶级会议，为CCF A类会议。从1982年创办至今的三十多年里，ASPLOS推动了多项计算机系统技术的发展，包括（但不限于）RISC、RAID、大规模多处理器、Cluster架构和网络存储等。

Many important graph applications are iterative algorithms that repeatedly process the input graph until convergence. For such algorithms, graph abstraction is an important technique: although much smaller than the original graph, it can bootstrap an initial result that can significantly accelerate the final convergence speed, leading to a better overall performance. However, existing graph abstraction techniques typically assume either fully in-memory or distributed environment, which leads to many obstacles preventing the application to an out-of-core graph processing system. In this paper, we propose Wonderland, a novel out-of-core graph processing system based on abstraction. Wonderland has three unique features: 1) A simple method applicable to out-of-core systems allowing users to extract effective abstractions from the original graph with acceptable cost and a specific memory limit; 2) Abstraction-enabled information propagation, where an abstraction can be used as a bridge over the disjoint on-disk graph partitions; 3) Abstraction guided priority scheduling, where an abstraction can infer the better priority-based order in processing on-disk graph partitions. Wonderland is a significant advance over the state-of-the-art because it not only makes graph abstraction feasible to out-of-core systems, but also broadens the applications of the concept in important ways. Evaluation results of Wonderland reveal that Wonderland achieves a drastic speedup over the other state-of-the-art systems, up to two orders of magnitude for certain cases.
Wonderland: A Novel Abstraction-Based Out-Of-Core Graph Processing System | Request PDF. Available from: https://www.researchgate.net/publication/323950961_Wonderland_A_Novel_Abstraction-Based_Out-Of-Core_Graph_Processing_System [accessed Aug 30 2018].

云计算环境下的大规模图数据处理技术：https://blog.csdn.net/zcf1002797280/article/details/50707482

大规模图计算系统综述：https://blog.csdn.net/qq_21125183/article/details/80671547

图计算：一张图秒级洞察千亿级复杂关系：http://www.cnblogs.com/chengxuyuanbrother/p/9552462.html

《大规模图数据匹配技术综述》——笔记：https://blog.csdn.net/u013319237/article/details/58075976

图计算系统进展和展望：https://blog.csdn.net/heyc861221/article/details/80126827

大数据图处理系统总结：https://greeensy.github.io/2014/06/20/Graph-computing/

人工智能大数据文本分析浅谈——基于公安笔录文本分析：https://tieba.baidu.com/p/5626600547?red_tag=2244610809 （+语义理解+自然语言处理在xxx中的应用）