Energy consumption is an important concern in modern multicore processors. The energy consumed by a multicore processor during the execution of an application can be minimized by tuning the hardware state utilizing knobs such as frequency, voltage etc. The existing theoretical work on energy minimization using Global DVFS (Dynamic Voltage and Frequency Scaling), despite being thorough, ignores the time and the energy consumed by the CPU on memory accesses and the dynamic energy consumed by the idle cores. This article presents an analytical energy-performance model for parallel workloads that accounts for the time and the energy consumed by the CPU chip on memory accesses in addition to the time and energy consumed by the CPU on CPU instructions. In addition, the model we present also accounts for the dynamic energy consumed by the idle cores. The existing work on global DVFS for parallel workloads shows that using a single frequency for the entire duration of a parallel application is not energy optimal and that varying the frequency according to the changes in the parallelism of the workload can save energy. We present an analytical framework around our energy-performance model to predict the operating frequencies (that depend upon the amount of parallelism) for global DVFS that minimize the overall CPU energy consumption. We show how the optimal frequencies in our model differ from the optimal frequencies in a model that does not account for memory accesses. We further show how the memory intensity of an application affects the optimal frequencies.
|Title of host publication||Proceedings of 28th ACM Symposium on Parallelism in Algorithms and Architectures|
|Publication status||Published - 11 Jul 2016|
|Event||28th ACM International Symposium on Parallelism in Algorithms and Architectures - Asilomar Beach, California, United States|
Duration: 11 Jul 2016 → 13 Jul 2016
|Conference||28th ACM International Symposium on Parallelism in Algorithms and Architectures|
|Period||11/07/2016 → 13/07/2016|
Trehan, C., Karakonstantis, G., Vandierendonck, H., & Nikolopoulos, D. (2016). Energy Optimization of Memory Intensive Parallel Workloads. In Proceedings of 28th ACM Symposium on Parallelism in Algorithms and Architectures (pp. 251-252). ACM. https://doi.org/10.1145/2935764.2935811