Intelligent adaptation of hardware knobs for improving performance and power consumption
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. Differ...
| Autores: | , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/329376 |
| Acceso en línea: | https://hdl.handle.net/2117/329376 https://dx.doi.org/10.1109/TC.2020.2980230 |
| Access Level: | acceso abierto |
| Palabra clave: | Parallel programming (Computer science) Microprocessors High performance computing HPC Parallel programming Runtime SMT Data prefetcher DVFS Programació en paral·lel (Informàtica) Microprocessadors Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors |
| Sumario: | 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. |
|---|