Journal of Computer Science and Technology ›› 2020, Vol. 35 ›› Issue (1): 47-60.doi: 10.1007/s11390-020-9798-5

Special Issue: Computer Architecture and Systems

• Special Section on Selected I/O Technologies for High-Performance Computing and Data Analytics • Previous Articles     Next Articles

Lessons Learned from Optimizing the Sunway Storage System for Higher Application I/O Performance

Qi Chen1, Kang Chen1, Zuo-Ning Chen2, Fellow, CCF, Wei Xue1, Xu Ji1,3, Bin Yang3,4        

  1. 1 Department of Computer Science and Technology, Beijing National Research Center for Information Science and Technology(BNRist), Tsinghua University, Beijing 100084, China;
    2 Chinese Academy of Engineering, Beijing 100088, China;
    3 National Supercomputing Center in Wuxi, Wuxi 214100, China;
    4 School of Software, Shandong University, Jinan 250101, China
  • Received:2019-07-30 Revised:2019-11-28 Online:2020-01-05 Published:2020-01-14
  • About author:Qi Chen currently is a Ph.D. student of the Department of Computer Science and Technology at Tsinghua University, Beijing. He received his Master's degree in computer architecture from Huazhong University of Science and Technology, Wuhan, in 2013. His research interests include storage architecture for high-performance computing and distributed file systems.
  • Supported by:
    This work is supported by the National Key Research and Development Program of China under Grant No. 2016YFB1000504, the Natural Science Foundation of China under Grant Nos. 61433008, 61373145, and 61572280, and China Postdoctoral Science Foundation under Grant No. 2018M630162.

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It is hard for applications to make full utilization of the peak bandwidth of the storage system in highperformance computers because of I/O interferences, storage resource misallocations and complex long I/O paths. We performed several studies to bridge this gap in the Sunway storage system, which serves the supercomputer Sunway TaihuLight. To locate these issues and connections between them, an end-to-end performance monitoring and diagnosis tool was developed to understand I/O behaviors of applications and the system. With the help of the tool, we were about to find out the root causes of such performance barriers at the I/O forwarding layer and the parallel file system layer. An application-aware I/O forwarding allocation framework was used to address the I/O interferences and resource misallocations at the I/O forwarding layer. A performance-aware data placement mechanism was proposed to mitigate the impact of I/O interferences and performance variations of storage devices in the PFS. Together, applications obtained much better I/O performance. During the process, we also proposed a lightweight storage stack to shorten the I/O path of applications with N-N I/O pattern. This paper summarizes these studies and presents the lessons learned from the process.

Key words: high performance computing; I/O interference; parallel file system; performance optimization; resource misallocation;

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