TY - GEN
T1 - Pipeline-based parallel framework for mass file processing
AU - Liu, Tao
AU - Liu, Yi
AU - Wang, Qingquan
AU - Wang, Xiangrong
AU - Gao, Fei
AU - Qian, Depei
PY - 2013
Y1 - 2013
N2 - Currently, there exists billions of files on the Internet, such as pictures, web pages, audio and video files, etc., and the number is still growing rapidly. These huge amount of files need to be processed by some applications as quickly as possible with parallel processing. With the increasing of cores in processors, parallel programming becomes more complex. The behavior that multiple parallel processes/threads access files simultaneously may interfere with each other and cause extra performance loss. Consequently, this paper proposes a pipeline-based parallel framework for mass file processing, in which file processing is divided into multiple stages to compose a pipeline. Files flow through these stages one by one, and the interferences in file-accessing are avoided. Moreover, the parallel programming can be simplified by means of parallel frameworks and programming interfaces. Experiments with one real-world application and some micro-benchmarks show that the framework can efficiently improve system performance.
AB - Currently, there exists billions of files on the Internet, such as pictures, web pages, audio and video files, etc., and the number is still growing rapidly. These huge amount of files need to be processed by some applications as quickly as possible with parallel processing. With the increasing of cores in processors, parallel programming becomes more complex. The behavior that multiple parallel processes/threads access files simultaneously may interfere with each other and cause extra performance loss. Consequently, this paper proposes a pipeline-based parallel framework for mass file processing, in which file processing is divided into multiple stages to compose a pipeline. Files flow through these stages one by one, and the interferences in file-accessing are avoided. Moreover, the parallel programming can be simplified by means of parallel frameworks and programming interfaces. Experiments with one real-world application and some micro-benchmarks show that the framework can efficiently improve system performance.
KW - Big data
KW - Parallel programming
KW - Pipeline framework
UR - https://www.scopus.com/pages/publications/84893580911
U2 - 10.1109/CSC.2013.15
DO - 10.1109/CSC.2013.15
M3 - 会议稿件
AN - SCOPUS:84893580911
SN - 9780769551579
T3 - Proceedings - 2013 International Conference on Cloud and Service Computing, CSC 2013
SP - 42
EP - 48
BT - Proceedings - 2013 International Conference on Cloud and Service Computing, CSC 2013
PB - IEEE Computer Society
T2 - 2013 International Conference on Cloud and Service Computing, CSC 2013
Y2 - 4 November 2013 through 6 November 2013
ER -