Modern Multicore CPUs are not Energy Proportional: Opportunity for Bi-objective Optimization for Performance and Energy

TL;DR

Modern multicore CPUs do not exhibit energy proportionality, enabling the need for bi-objective optimization to balance performance and energy consumption in multithreaded applications.

Contribution

This paper introduces the first application-level bi-objective optimization method for multithreaded applications on multicore CPUs, considering performance and energy.

Findings

Optimization for performance increases energy consumption by up to 89%.

Energy-focused optimization can degrade performance by up to 49%.

The method finds up to 11 Pareto-optimal solutions.

Abstract

Energy proportionality is the key design goal followed by architects of modern multicore CPUs. One of its implications is that optimization of an application for performance will also optimize it for energy. In this work, we show that energy proportionality does not hold true for multicore CPUs. This finding creates the opportunity for bi-objective optimization of applications for performance and energy. We propose and study the first application-level method for bi-objective optimization of multithreaded data-parallel applications for performance and energy. The method uses two decision variables, the number of identical multithreaded kernels (threadgroups) executing the application and the number of threads in each threadgroup, with the workload always partitioned equally between the threadgroups. We experimentally demonstrate the efficiency of the method using four highly optimized multithreaded data-parallel applications, 2D fast Fourier transform based on FFTW and Intel MKL, and dense matrix-matrix multiplication using OpenBLAS and Intel MKL. Four modern multicore CPUs are used in the experiments. The experiments show that optimization for performance alone results in the increase in dynamic energy consumption by up to 89% and optimization for dynamic energy alone degrades the performance by up to 49%. By solving the bi-objective optimization problem, the method determines up to 11 globally Pareto-optimal solutions. Finally, we propose a qualitative dynamic energy model employing performance monitoring counters as parameters, which we use to explain the discovered energy nonproportionality and the Pareto-optimal solutions determined by our method. The model shows that the energy nonproportionality in our case is due to the activity of the data translation lookaside buffer (dTLB), which is disproportionately energy expensive.