Name
Abstract | ||
|---|---|---|
The exponential growth of computing power on leadership scale computing platforms imposes grand challenge to scientific applications' input/output I/O performance. To bridge the performance gap between computation and I/O, various parallel I/O libraries have been developed and adopted by computer scientists. These libraries enhance the I/O parallelism by allowing multiple processes to concurrently access the shared data set. Meanwhile, they are integrated with a set of I/O optimization strategies such as data sieving and two-phase I/O to better exploit the supplied bandwidth of the underlying parallel file system. Most of these techniques are optimized for the access on a single bundle of variables generated by the scientific applications during the I/O phase, which is stored in the form of file. Few of these techniques focus on cross-bundle I/O optimizations. In this article, we investigate the potential benefit from cross-bundle I/O aggregation. Based on the analysis of the I/O patterns of a mission-critical scientific application named the Goddard Earth Observing System, version 5 GEOS-5, we propose a Bundle-based PARallel Aggregation BPAR framework with three partitioning schemes to improve its I/O performance as well as the I/O performance of a broad range of other scientific applications. Our experiment result reveals that BPAR can deliver 2.1× I/O performance improvement over the baseline GEOS-5, and it is very promising in accelerating scientific applications' I/O performance on various computing platforms. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1177/1094342015618017 | IJHPCA |
Keywords | Field | DocType |
Aggregation, GEOS-5, high-performance computing, parallel I, O, scientific application, storage system | File system,Supercomputer,Computer science,Computer data storage,Parallel computing,Theoretical computer science,Input/output,Bandwidth (signal processing),Parallel I/O,Bundle,Performance improvement,Distributed computing | Journal |
Volume | Issue | ISSN |
30 | 2 | 1094-3420 |
Citations | PageRank | References |
2 | 0.38 | 31 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Teng Wang | 1 | 336 | 42.78 |
| Kevin Vasko | 2 | 7 | 1.15 |
| Zhuo Liu | 3 | 118 | 16.03 |
| Hui Chen | 4 | 2 | 0.38 |
| Weikuan Yu | 5 | 1042 | 77.40 |