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| Content Provider | ACM Digital Library |
|---|---|
| Author | Vazhkudai, Sudharshan S. Albing, Carl Atchley, Scott Zimmer, Christopher Gupta, Saurabh |
| Abstract | Job placement plays a pivotal role in application performance on supercomputers. We present a multi-faceted exploration to influence placement in extreme-scale systems, to improve network performance and decrease variability. In our first exploration, Scores, we developed a machine learning model that extracts features from a job's node-allocation and grades performance. This identified several important node-metrics that led to Dual-Ended scheduling, a means of reducing network contention without impacting utilization. In evaluations on the Titan supercomputer, we observed reductions in average hop-count by up to 50%. We also developed an improved node-layout strategy that targets a better balance between network latency and bandwidth, replacing the default ALPS layout on Titan that resulted in an average of 10% runtime improvement. Both of these efforts underscore the importance of a job placement strategy that is cognizant of workload mixture and network topology. |
| Starting Page | 1 |
| Ending Page | 11 |
| Page Count | 11 |
| File Format | |
| ISBN | 9781467388153 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-11-13 |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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