Name
Title | ||
|---|---|---|
CARTAD: Compiler-Assisted Reinforcement Learning for Thermal-Aware Task Scheduling and DVFS on Multicores |
Abstract | ||
|---|---|---|
As the power density of modern CPUs is gradually increasing, thermal management has become one of the primary concerns for multicore systems, where task scheduling and dynamic voltage/frequency scaling (DVFS) play a pivotal role in effectively managing the system temperature. In this article, we propose
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, a new reinforcement learning (RL)-based task scheduling and DVFS method for temperature minimization and latency guarantee on multicore systems. The novelty of
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framework is that we exploit the machine learning technique to analyze the applications’ intermediate representations (IRs) generated by a compiler and identify an important feature which is critical for predicting the application’s performance. With the newly explored feature, we construct an RL-based scheduler with the more effective state representation and reward function such that the system temperature can be minimized while guaranteeing applications’ latency. We implement and evaluate
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on real platforms in comparison with the state-of-the-art approaches. Experimental results show
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can reduce the maximum temperature by up to 16 °C and the average temperature by up to 10 °C. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCAD.2021.3095028 | IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems |
Keywords | DocType | Volume |
Compiler,dynamic voltage/frequency scaling (DVFS),reinforcement learning (RL),task scheduling | Journal | 41 |
Issue | ISSN | Citations |
6 | 0278-0070 | 0 |
PageRank | References | Authors |
0.34 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Di Liu | 1 | 1 | 1.71 |
| Shi-Gui Yang | 2 | 0 | 0.34 |
| Zhenli He | 3 | 2 | 1.72 |
| Mingxiong Zhao | 4 | 32 | 4.90 |
| Weichen Liu | 5 | 0 | 1.35 |