Hpc Energy Efficiency Analysis Evaluates Gromacs and Benchmarks on Intel Ice Lake, Sapphire Rapids, and A40/A100 GPUs

Understanding the energy consumption of high-performance computing systems presents a significant challenge as researchers strive for greater efficiency, and a new study tackles this problem head-on. Rafael Ravedutti Lucio Machado, Jan Eitzinger, Georg Hager, and Gerhard Wellein, all from the Erlangen National High Performance Computing Center, investigate the difficulties inherent in accurately measuring energy efficiency in these complex machines. Their work focuses on evaluating both synthetic benchmarks and the widely used Gromacs software package, employing detailed measurements obtained with specialised profiling tools on state-of-the-art CPUs and GPUs. By carefully examining the pitfalls encountered during experimentation, the team provides valuable insights and establishes best practices for future energy efficiency analysis, ultimately paving the way for more sustainable high-performance computing.

This paper examines the challenges of analysing energy efficiency in synthetic benchmarks and the Gromacs package on the Fritz and Alex HPC clusters. Experiments used MPI parallelism on full sockets of Intel Ice Lake and Sapphire Rapids CPUs, as well as Nvidia A40 and A100 GPUs. The authors highlight numerous challenges involved in obtaining meaningful energy measurements from modern applications like Gromacs and MD-Bench. Key challenges include dynamic frequency scaling, where processors adjust speed based on workload, making static power measurements unreliable, and measurement overhead caused by the sampling frequency and impact of energy counters. Limited access to detailed power data, often restricted by vendor tools, also presents difficulties.

To overcome these challenges, the authors propose several solutions. Complete affinity control, precisely managing core usage, is crucial for consistent measurements. Focusing on average frequency, rather than set frequencies, provides a more realistic picture of power consumption. A thorough measurement strategy is needed to account for measurement overhead and limitations. The authors advocate for open standards and accessible interfaces for accessing power data from both CPUs and GPUs. They also caution against relying solely on automated benchmark scripts, emphasizing the importance of understanding expected results for validation. Accurate energy efficiency analysis in HPC requires a nuanced approach, careful consideration of system dynamics, and a move towards more open and accessible measurement tools.

HPC Energy Efficiency, Benchmarks and Gromacs Simulations

This work presents a comprehensive analysis of energy efficiency in high-performance computing (HPC) systems, focusing on both synthetic benchmarks and the widely used molecular dynamics package, Gromacs. Experiments were conducted on the Fritz and Alex clusters, utilizing Intel Ice Lake and Sapphire Rapids CPUs, as well as Nvidia A40 and A100 GPUs, to meticulously evaluate power consumption and performance characteristics. Researchers employed tools like Likwid and Nvidia profiling utilities to gather detailed metrics, revealing critical insights into the energy behaviour of complex simulations. The study demonstrates the importance of understanding the differences between synthetic benchmarks and real-world applications like Gromacs when assessing energy efficiency.

Through systematic measurements, the team investigated how settings such as power-capping limits and frequency scaling affect performance and energy usage across various hardware configurations. Results show that careful control of these parameters is crucial for optimising energy efficiency without sacrificing computational speed. Analysis of Gromacs simulations revealed the energy demands of different computational phases, allowing researchers to pinpoint code sections responsible for the majority of energy consumption. By correlating performance metrics with these phases, the team identified opportunities for optimisation and improvement. This detailed characterisation of energy consumption, coupled with performance analysis, provides valuable insights for designing more sustainable and efficient HPC infrastructure.

Gromacs Energy Efficiency on HPC Clusters

This work presents a detailed analysis of energy efficiency when using the Gromacs molecular dynamics package on high-performance computing clusters. Researchers conducted experiments on both Intel CPUs and NVIDIA GPUs, employing a range of parallelisation techniques, and meticulously collected performance and power metrics using tools like Likwid. The results demonstrate the challenges inherent in accurately assessing energy efficiency in complex HPC environments, highlighting the need for careful experimental design and analysis. The team identified potential pitfalls in energy efficiency studies, particularly concerning the influence of hardware characteristics, algorithmic choices, and communication patterns on overall energy consumption.

They emphasise the importance of correlating power measurements with specific computational phases within applications to pinpoint areas for optimisation. While acknowledging the complexity of predicting energy usage, the authors suggest best practices for future research, advocating for consistent methodologies to overcome obstacles in this field. This study provides valuable insights into the difficulties of measuring and improving energy efficiency in large-scale simulations.