Name
Abstract | ||
|---|---|---|
Aggressive thermal management is a critical feature for high-end computing platforms, as worst-case thermal budgeting is becoming unaffordable. Reactive thermal management, which sets temperature thresholds to trigger thermal capping actions, is too “near-sighted,” and it may lead to severe performance degradation and thermal overshoots. More aggressive proactive thermal managements minimize performance penalty with smooth optimal control. These techniques require knowledge of thermal models, which have to be accurate and simple to make the controls effective, while keeping their complexity limited. In practice, these models are not provided by manufacturers, and in most cases, they strongly depend on the deployment environment. Hence, procedures to automatically derive thermal models in the field are needed. In this paper, we propose a gray-box procedure to learn a compact and physically consistent model for multicore chips. We leverage the physical consistency of the proposed model to tame the model complexity and to face large quantization noise in measurements. We exploit Output Error structures along with Levenberg–Marquardt and Least Squares optimization algorithms. We tackle the problem in a real-life contest: we developed a complete infrastructure for model building and thermal data collection in the Linux environment, and we tested it on an Intel Nehalem-based server CPU. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TC.2012.293 | Computers, IEEE Transactions |
Keywords | Field | DocType |
Linux,computational complexity,least mean squares methods,microprocessor chips,multiprocessing systems,optimal control,optimisation,performance evaluation,power aware computing,Intel Nehalem-based server CPU,Levenberg-Marquardt algorithms,Linux environment,effective gray-box identification procedure,high-end computing platforms,least squares optimization algorithms,model building,model complexity,multicore chips,multicore thermal modeling,output error structures,performance degradation,proactive thermal managements,quantization noise,reactive thermal management,smooth optimal control,thermal data collection,thermal management,thermal overshoots,triggerthermal capping actions,worst-case thermal budgeting,Thermal control,gray box,multicore,power model,system identification,thermal model | Power management,Optimal control,Computer science,Parallel computing,Model building,Real-time computing,Gray box testing,Deployment environment,System identification,Quantization (signal processing),Multi-core processor | Journal |
Volume | Issue | ISSN |
63 | 5 | 0018-9340 |
Citations | PageRank | References |
17 | 0.64 | 34 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesco Beneventi | 1 | 46 | 5.73 |
| Andrea Bartolini | 2 | 457 | 51.90 |
| Andrea Tilli | 3 | 221 | 17.74 |
| Luca Benini | 4 | 13116 | 1188.49 |