OPTKIT: Unleash the Green Potential of Your CPU Code

The analysis of energy consumption in software has traditionally taken a back seat to performance analysis. However, increasing environmental concerns, power and cooling limitations in expanding HPC systems and data centers, and the need for energy efficiency in power-constrained devices such as mobile phones, tablets, laptops, and IoT devices have brought significant focus to this area. While performance analysis has a wide range of tools, those specifically designed for software energy efficiency are scarce. This study introduces the Optimizer Toolkit (optkit), a dedicated library and toolset designed for the energy analysis and optimization of software runtime. optkit utilizes perf_event_open kernel call for PMU and RAPL, and msr-safe for cpu frequency operations. Inside the tool, there are Query* classes where the user can query anything regarding cpu, energy, and performance before taking any action. It includes useful utility tools that help users streamline the optimization process. In this work, we leverage optkit to evaluate the potential for reducing a system's energy consumption by strong-scaling core numbers in a given application. We then use these data to further reduce energy consumption through co-scheduling applications. In another use-case, we investigate the impact of CPU frequency on energy consumption and develop a methodology to dynamically detect the optimal frequency at runtime and adjusts frequencies based on execution phases to maximize energy savings. We find that not all applications scale linearly, and allocating appropriate resources yields better energy savings. Co-scheduling applications by using unassigned resources often saves more energy than serial execution counterparts. Although manually finding the best frequency offers the highest energy savings, our automated approach achieves up to 31.83% energy savings compared to normal execution of applications. The optkit tool is publicly available on GitHub