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UNIO: A Unified I/O System Framework for Hybrid Scientific Workflow

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Cloud Computing and Big Data (CloudCom-Asia 2015)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9106))

Abstract

Recent years have seen an increasing number of Hybrid Scientific Applications. They often consist of one HPC simulation program along with its corresponding data analytics programs. Unfortunately, current computing platform settings do not accommodate this emerging workflow very well. This is mainly because HPC simulation programs store output data into a dedicated storage cluster equipped with Parallel File System (PFS). To perform analytics on data generated by simulation, data has to be migrated from storage cluster to compute cluster. This data migration could introduce severe delay which is especially true given an ever-increasing data size.

While the scale-up supercomputers equipped with dedicated PFS storage cluster still represent the mainstream HPC, ever increasing scale-out small-medium sized HPC clusters have been supplied to facilitate hybrid scientific workflow applications in fast-growing cloud computing infrastructures such as Amazon cluster compute instances. Different from traditional supercomputer setting, the limited network bandwidth in scale-out HPC clusters makes the data migration prohibitively expensive. To attack the problem, we develop a Unified I/O System Framework (UNIO) to avoid such migration overhead for scale-out small-medium sized HPC clusters. Our main idea is to enable both HPC simulation programs and analytics programs to run atop one unified file system, e.g. data-intensive file system (DIFS in brief). In UNIO, an I/O middle-ware component allows original HPC simulation programs to execute direct I/O operations over DIFS without any porting effort, while an I/O scheduler dynamically smoothes out both disk write and read traffic for both simulation and analysis programs. By experimenting with a real-world scientific workflow over a 46-node UNIO prototype, we found that UNIO is able to achieve comparable read/write I/O performance in small-medium sized HPC clusters equipped with parallel file system. More importantly, since UNIO completely avoids the most expensive data movement overhead, it achieves up to 3x speedups for hybrid scientific workflow applications compared with current solutions.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Central Florida, Orlando, USA

    Dan Huang, Jiangling Yin, Jun Wang, Xuhong Zhang, Junyao Zhang & Jian Zhou

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  1. Dan Huang
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  2. Jiangling Yin
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  3. Jun Wang
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  4. Xuhong Zhang
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  5. Junyao Zhang
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  6. Jian Zhou
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Correspondence to Dan Huang .

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Editors and Affiliations

  1. School of Computer Science and Tech., Huazhong Univ. of Science and Technology, Wuhan, China

    Weizhong Qiang

  2. College of Mathematics and Computer Sci., Fuzhou University, Fuzhou, China

    Xianghan Zheng

  3. Dept. of Computer Scie and Informat. Eng, Chung Hua University, Hsinchu, Taiwan

    Ching-Hsien Hsu

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Huang, D., Yin, J., Wang, J., Zhang, X., Zhang, J., Zhou, J. (2015). UNIO: A Unified I/O System Framework for Hybrid Scientific Workflow. In: Qiang, W., Zheng, X., Hsu, CH. (eds) Cloud Computing and Big Data. CloudCom-Asia 2015. Lecture Notes in Computer Science(), vol 9106. Springer, Cham. https://doi.org/10.1007/978-3-319-28430-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-28430-9_8

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