• DocumentCode
    1791537
  • Title

    FusionFS: Toward supporting data-intensive scientific applications on extreme-scale high-performance computing systems

  • Author

    Dongfang Zhao ; Zhao Zhang ; Xiaobing Zhou ; Tonglin Li ; Ke Wang ; Kimpe, Dries ; Carns, Philip ; Ross, Robert ; Raicu, Ioan

  • Author_Institution
    Illinois Inst. of Technol., Chicago, IL, USA
  • fYear
    2014
  • fDate
    27-30 Oct. 2014
  • Firstpage
    61
  • Lastpage
    70
  • Abstract
    State-of-the-art, yet decades-old, architecture of high-performance computing systems has its compute and storage resources separated. It thus is limited for modern data-intensive scientific applications because every I/O needs to be transferred via the network between the compute and storage resources. In this paper we propose an architecture that hss a distributed storage layer local to the compute nodes. This layer is responsible for most of the I/O operations and saves extreme amounts of data movement between compute and storage resources. We have designed and implemented a system prototype of this architecture - which we call the FusionFS distributed file system - to support metadata-intensive and write-intensive operations, both of which are critical to the I/O performance of scientific applications. FusionFS has been deployed and evaluated on up to 16K compute nodes of an IBM Blue Gene/P supercomputer, showing more than an order of magnitude performance improvement over other popular file systems such as GPFS, PVFS, and HDFS.
  • Keywords
    distributed databases; meta data; parallel processing; storage management; FusionFS distributed file system; GPFS; HDFS; IBM Blue Gene-P supercomputer; PVFS; data-intensive; distributed storage layer; high-performance computing systems; metadata-intensive; storage resources; write-intensive operations; Computer architecture; Distributed databases; Fuses; Protocols; Servers; Supercomputers; Throughput;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (Big Data), 2014 IEEE International Conference on
  • Conference_Location
    Washington, DC
  • Type

    conf

  • DOI
    10.1109/BigData.2014.7004214
  • Filename
    7004214