This paper presents an improved solution for optimizing energy usage energy-performance trade-offs by means of power capping. We propose a new version of the DEPO tool that supports periodic tuning for dynamically changing workloads, targeted ultimately at deployment in the cloud at the Centre of Informatics Tricity Academic Supercomputer and networK, Gdańsk, Poland. We validated the approach using 27 experimental scenarios by executing three different applications in succession, generated as permutations with repetition, and compared the results to base runs without power capping. Across the experiments, periodic tuning delivers greater savings in energy consumption or energy-delay product (EDP) than singleshot tuning with a fixed tuning duration, eliminating the need for manual selection of that duration. The method also exhibits higher overall stability across all tested application orderings: Integer Sort (IS), Scalar Pentadiagonal Solver (SP), and Unstructured Adaptive (UA) from the NAS Parallel Benchmarks (NPB) suite. Evaluations were performed on two dual-socket servers equipped with 2x Intel Xeon Silver 4316 (Ice Lake) and 2x Intel Xeon Gold 6130 (Skylake) CPUs. The software is available as open source.

Abstract In the article we propose an automatic power capping software tool DEPO that allows one to perform runtime optimization of performance and energy related metrics. For an assumed application model with an initialization phase followed by a running phase with uniform compute and memory intensity, the tool performs automatic tuning engaging one of the two exploration algorithms—linear search (LS) and golden section search (GSS), finds a power cap optimizing a given metric and sets it for the remaining computations. The considered metrics include energy (E), energy‐delay sum, energy‐delay product. We present experimental results obtained for a set of benchmarks that differ in compute and memory intensity—parallel custom built OpenMP implementations of: numerical integration, heat distribution simulation (HEAT), fast Fourier transform (FFT), and additionally NAS parallel benchmarks: CG, MG, BT, SP, and LU. Tests were performed using multi‐core CPUs that are representatives of modern servers and the desktop family: 2 Intel Xeon E5‐2670 v3 CPU (Haswell‐EP) and Intel i7‐9700K CPU (Coffee Lake). The results show that our approach enabled considerable improvements for the tested metrics, for example, for HEAT and Coffee Lake we minimized energy by 50% at the cost of a 15% increase in execution time (LS), for FFT energy was minimized by 40% at a 25.5% increase in execution time (GSS), for SP and Haswell energy was minimized by 25% at the cost of an 18.5% time increase and for Coffee Lake energy was decreased by 56% with a 12% time increase.

In the paper we present extensive results from analyzing energy/performance trade-offs with power capping observed on four different modern CPUs, for three different parallel applications such as 2D heat distribution, numerical integration and Fast Fourier Transform.The CPU tested represent both multi-core type CPUs such as Intel R Xeon R E5, desktop and mobile i7 as well as many-core Intel R Xeon Phi TM x200 but also server, desktop and mobile solutions used widely nowadays.We show that using enforced power caps we can find points of lower than default energy consumption but mostly for desktop and mobile solutions at the cost of increased execution time.We show with particular numbers how energy consumed, power consumption and execution time change for the point of minimum energy used versus the default configuration with no power limit, for each application and each tested CPU.