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| Content Provider | ACM Digital Library |
|---|---|
| Author | Bao, Bin Shen, Kai Xiang, Xiaoya Ding, Chen |
| Abstract | Because of the interference in the shared cache on multicore processors, the performance of a program can be severely affected by its co-running programs. If job scheduling does not consider how a group of tasks utilize cache, the performance may degrade significantly, and the degradation usually varies sizably and unpredictably from run to run. In this paper, we use trace-based program locality analysis and make it efficient enough for dynamic use. We show a complete on-line system for periodically measuring the parallel execution, predicting and ranking cache interference for all co-run choices, and reorganizing programs based on the prediction. We test our system on floating-point and mixed integer and floating-point workloads composed of SPEC 2006 benchmarks and compare with the default Linux job scheduler to show the benefit of the new system in improving performance and reducing performance variation. |
| Starting Page | 603 |
| Ending Page | 611 |
| Page Count | 9 |
| File Format | |
| ISBN | 9780769546919 |
| DOI | 10.1109/CCGrid.2012.139 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-05-13 |
| Access Restriction | Subscribed |
| Subject Keyword | Multicore task grouping online program locality analysis lifetime sampling |
| Content Type | Text |
| Resource Type | Article |
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