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  1. Proceedings of the 2014 International Workshop on Data Intensive Scalable Computing Systems (DISCS '14)
  2. BPAR: a bundle-based parallel aggregation framework for decoupled I/O execution
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BPAR: a bundle-based parallel aggregation framework for decoupled I/O execution

Content Provider ACM Digital Library
Author Vasko, Kevin Chen, Hui Yu, Weikuan Liu, Zhuo Wang, Teng
Abstract In today's "Big Data" era, developers have adopted I/O techniques such as MPI-IO, Parallel NetCDF and HDF5 to garner enough performance to manage the vast amount of data that scientific applications require. These I/O techniques offer parallel access to shared datasets and together with a set of optimizations such as data sieving and two-phase I/O to boost I/O throughput. While most of these techniques focus on optimizing the access pattern on a single file or file extent, few of these techniques consider cross-file I/O optimizations. This paper aims to explore the potential benefit from cross-file I/O aggregation. We propose a Bundle-based PARallel Aggregation framework (BPAR) and design three partitioning schemes under such framework that targets at improving the I/O performance of a mission-critical application GEOS-5, as well as a broad range of other scientific applications. The results of our experiments reveal that BPAR can achieve on average 2.1x performance improvement over the baseline GEOS-5.
Starting Page 25
Ending Page 32
Page Count 8
File Format PDF
ISBN 9781479970384
DOI 10.1109/DISCS.2014.6
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-11-16
Access Restriction Subscribed
Content Type Text
Resource Type Article
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