Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (3): 690-706.doi: 10.1007/s11390-019-1936-6

Special Issue: Computer Architecture and Systems

• Computer Architecture and Systems • Previous Articles    

Cacheap: Portable and Collaborative I/O Optimization for Graph Processing

Peng Zhao1,2, Student Member, CCF, Chen Ding3, Member, ACM, IEEE, Lei Liu1,*, Member, CCF, Jiping Yu4, Wentao Han4, Member, CCF, ACM, IEEE, Xiao-Bing Feng1,2, Member, CCF, ACM, IEEE   

  1. 1 State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences Beijing 100190, China;
    2 University of Chinese Academy of Sciences, Beijing 100049, China;
    3 Department of Computer Science, University of Rochester, Rochester 14623, U.S.A.;
    4 Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
  • Received:2018-05-09 Revised:2019-03-19 Online:2019-05-05 Published:2019-05-06
  • Contact: Lei Liu E-mail:liulei@ict.ac.cn
  • About author:Peng Zhao received his B.S. degree in computer science and technology from Jilin University, Changchun, in 2013. Currently he is a Ph.D. candidate of Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), Beijing. His research interests include parallel and distributed programming models and graph processing systems.
  • Supported by:
    This work is supported by the National Key Research and Development Program of China under Grant No. 2017YFB1003103, the National Natural Science Foundation of China under Grant Nos. 61432018, 61432016, 61332009, and 61521092, the National Science Foundation of USA under Contract Nos. CCF-1717877 and CCF-1629376, and an IBM CAS Faculty Fellowship.

Increasingly there is a need to process graphs that are larger than the available memory on today's machines. Many systems have been developed with graph representations that are efficient and compact for out-of-core processing. A necessary task in these systems is memory management. This paper presents a system called Cacheap which automatically and efficiently manages the available memory to maximize the speed of graph processing, minimize the amount of disk access, and maximize the utilization of memory for graph data. It has a simple interface that can be easily adopted by existing graph engines. The paper describes the new system, uses it in recent graph engines, and demonstrates its integer factor improvements in the speed of large-scale graph processing.

Key words: out-of-core graph processing system, I/O optimization, memory cache, graph analytics, locality

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