Intelligent Adaptation of Hardware Knobs for Improving Performance and Power Consumption
| Autor: | Alexandre E. Eichenberger, Cristobal Ortega, Ramon Bertran, Marc Casas, Miquel Moreto, Pradip Bose, Lluc Alvarez, Alper Buyuktosunoglu |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Barcelona Supercomputing Center, Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Parallel programming Parallel programming (Computer science) 02 engineering and technology Programació en paral·lel (Informàtica) Data prefetcher Theoretical Computer Science Low-power electronics 0202 electrical engineering electronic engineering information engineering DVFS Adaptation (computer science) Microprocessors Informàtica::Arquitectura de computadors [Àrees temàtiques de la UPC] Profiling (computer programming) Multi-core processor Hardware_MEMORYSTRUCTURES business.industry Process (computing) Workload 020202 computer hardware & architecture Power (physics) Runtime Computational Theory and Mathematics Hardware and Architecture SMT HPC Metric (mathematics) Microprocessadors High performance computing business Software Computer hardware |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| ISSN: | 2326-3814 0018-9340 |
| DOI: | 10.1109/tc.2020.2980230 |
| Popis: | Current microprocessors include several knobs to modify the hardware behavior in order to improve performance, power, and energy under different workload demands. An impractical and time consuming offline profiling is needed to evaluate the design space to find the optimal knob configuration. Different knobs are typically configured in a decoupled manner to avoid the time-consuming offline profiling process. This can often lead to underperforming configurations and conflicting decisions that jeopardize system power-performance efficiency. Thus, a dynamic management of the different hardware knobs is necessary to find the knob configuration that maximizes system power-performance efficiency without the burden of offline profiling. In this paper, we propose libPRISM, an infrastructure that enables the transparent management of multiple hardware knobs in order to adapt the system to the evolving demands of hardware resources in different workloads. libPRISM can minimize execution time, energy-delay product or power consumption by dynamically managing the SMT level, the data prefetcher, and the DVFS hardware knobs. Overall, the proposed solutions increase performance up to 130% (16.9% on average), reduce energy-delay product up to 80%, and reduce power consumption up to 33% depending on the target metric compared to the default knob configuration of the system. This work has been supported by the RoMoL ERC Advanced Grant (GA 321253), by the European HiPEAC Network of Excellence, by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P), by Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272) and by IBM/BSC Deep Learning Center initiative.This research was developed in part with funding from the Defense Advanced Research Projects Agency (DARPA). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|