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
Link To Document :
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