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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jialin Liu Yong Chen Yi Zhuang |
| Copyright Year | 2013 |
| Description | Author affiliation: Dept. of Comput. Sci., Texas Tech Univ., Lubbock, TX, USA (Jialin Liu; Yong Chen; Yi Zhuang) |
| Abstract | The non-contiguous access pattern of many scientific applications results in a large number of I/O requests, which can seriously limit the data-access performance. Collective I/O has been widely used to address this issue. However, the performance of collective I/O could be dramatically degraded in today's high-performance computing system due to the increasing shuffle cost caused by highly concurrent data accesses. This situation tends to be even worse as many applications become more and more data intensive. Previous research has primarily focused on optimizing I/O access cost in collective I/O but largely ignored the shuffle cost involved. In this study, we propose a new hierarchical I/O scheduling (HIO) algorithm to address the increasing shuffle cost in collective I/O. The fundamental idea is to schedule applications' I/O requests based on a shuffle cost analysis to achieve the optimal overall performance, instead of achieving optimal I/O accesses only. The algorithm is currently evaluated with the MPICH2 andPVFS2. Both theoretical analysis and experimental tests show that the proposed hierarchical I/O scheduling has a potential in addressing the degraded performance issue of collective I/O with highly concurrent accesses. |
| Starting Page | 211 |
| Ending Page | 218 |
| File Size | 692593 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781467364652 |
| e-ISBN | 9780769549965 |
| DOI | 10.1109/CCGrid.2013.30 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-05-13 |
| Publisher Place | Netherlands |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Scheduling Delays Scheduling algorithms Servers Algorithm design and analysis Equations data intensive computing collective I/O scheduling high-performance computing big data |
| Content Type | Text |
| Resource Type | Article |
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