libPRISM: an intelligent adaptation of prefetch and SMT levels
Document typeConference report
PublisherAssociation for Computing Machinery (ACM)
Rights accessOpen Access
European Commission's projectROMOL - Riding on Moore's Law (EC-FP7-321253)
Current microprocessors include several knobs to modify the hardware behavior in order to improve performance 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 sometimes to 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. We use libPRISM to implement a policy that maximizes system performance without degrading energy efficiency by dynamically managing the SMT level and prefetcher hardware knobs of an IBM POWER8 system. We evaluate our solution using 24 applications from 3 different parallel benchmarks suites without the need of offline profiling or workload modification. Overall, the solution increases performance up to 220% (15.4% on average) and reduces dynamic power consumption up to 13% (2.0% on average) when compared to the static default knob configuration.
CitationOrtega, C., Moreto, M., Casas, M., Bertran, R., Buyuktosunoglu, A., Eichenberger, A., Bose, P. libPRISM: an intelligent adaptation of prefetch and SMT levels. A: International Conference on Supercomputing. "ICS'17: proceedings of the International Conference on Supercomputing". Chicago, Illinois: Association for Computing Machinery (ACM), 2017, p. 28:1-28:10.