Name
Abstract | ||
|---|---|---|
The data-intensive scientific applications running on high-end computing system depend on parallel file systems for high-speed data input/output. In most parallel file systems, a file is partitioned into multiple subfiles with a view to allowing it to be accessed concurrently. An important factor in the file partition is the stripe size. However, while working well for certain applications, most existing schemes for determining the stripe size for a file still lack the ability to handle highly concurrent data accesses, which is typical for most parallel scientific applications. To address this problem, this paper presents an analytic model to assess the performance of highly concurrent data accesses at first, and then it describes how to apply this model to select the stripe size of a file. Experimental results demonstrate that the accuracy of the analytic model is around 87.89% and the stripe size selected with it can improve the aggregated I/O bandwidth of FLASH I/O up to 5.8 times compared with well-known methods. This paper also discusses how to incorporate our method into real-world parallel file systems. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/Grid.2012.11 | GRID |
Keywords | Field | DocType |
data-intensive scientific applications,parallel processing,new file-specific stripe size,high-speed data input/output,highly concurrent data accesses,analytic model,file-specific stripe size selection method,file-specific stripe size,real-world parallel file system,stripe size,data-intensive scientific application,high-speed data input,concurrent data access,natural sciences computing,o bandwidth,highly concurrent data access,aggregated i/o bandwidth,file organisation,selection method,parallel scientific applications,parallel scientific application,flash i/o,file partition,performance optimization,parallel file systems,parallel file system,high-end computing system,optimization,servers,mathematical model,concurrent computing | Self-certifying File System,Computer science,Parallel computing,Device file,Class implementation file,Real-time computing,Torrent file,Unix file types,Indexed file,Memory-mapped file,File system fragmentation,Distributed computing | Conference |
ISSN | ISBN | Citations |
1550-5510 | 978-1-4673-2901-9 | 10 |
PageRank | References | Authors |
0.53 | 22 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Dong | 1 | 25 | 2.50 |
| Xiuqiao Li | 2 | 51 | 5.74 |
| Limin Xiao | 3 | 107 | 28.51 |
| Li Ruan | 4 | 123 | 25.10 |