Name
Abstract | ||
|---|---|---|
Predicting performance degradation of a GPU application at co-location on a spatial multitasking GPU without prior application knowledge is essential in public Clouds. Prior work mainly targets CPU co-location, and is inaccurate and/or inefficient for predicting performance of applications at co-location on spatial multitasking GPUs. Our investigation shows that hardware event statistics caused by co-located applications strongly correlate with their slowdowns. Based on this observation, we present Themis with a kernel slowdown model (Themis-KSM), which performs precise and efficient online application slowdown prediction without prior application knowledge. The kernel slowdown model is trained offline. When new applications co-run, Themis-KSM collects event statistics and predicts their slowdowns simultaneously. In addition, we also propose a two-stage slowdown prediction mechanism (Themis-TSP) for real-system GPUs without any hardware modification. Our evaluation shows that Themis has negligible runtime overhead, and both Themis-KSM and Themis-TSP can precisely predict application-level slowdown with prediction error smaller than 9.5% and 12.8%, respectively. Based on Themis, we also implement an SM allocation engine to rein in application slowdown at co-location. Case studies show that the engine successfully enforces fair sharing and QoS. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.jpdc.2020.03.009 | Journal of Parallel and Distributed Computing |
Keywords | DocType | Volume |
Spatial Multitasking GPU,Sharing GPU,Co-location,Slowdown prediction,Performance prediction | Journal | 141 |
ISSN | Citations | PageRank |
0743-7315 | 3 | 0.38 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mengze Wei | 1 | 3 | 0.38 |
| Wenyi Zhao | 2 | 3 | 0.38 |
| Quan Chen | 3 | 175 | 21.86 |
| Hao Dai | 4 | 3 | 0.38 |
| Jingwen Leng | 5 | 49 | 12.97 |
| Chao Li | 6 | 344 | 37.85 |
| Wenli Zheng | 7 | 4 | 1.09 |
| Minyi Guo | 8 | 35 | 14.13 |