Power-Efficient Computing: Experiences from the COSA Project

Abstract

Energy consumption is today one of the most relevant issues in operating HPC systems for scientific applications. The use of unconventional computing systems is therefore of great interest for several scientific communities looking for a better tradeoff between time-to-solution and energy-to-solution. In this context, the performance assessment of processors with a high ratio of performance per watt is necessary to understand how to realize energy-efficient computing systems for scientific applications, using this class of processors. Computing On SOC Architecture (COSA) is a three-year project (2015)(2016)(2017) funded by the Scientific Commission V of the Italian Institute for Nuclear Physics (INFN), which aims to investigate the performance and the total cost of ownership offered by computing systems based on commodity low-power Systems on Chip (SoCs) and high energyefficient systems based on GP-GPUs. In this work, we present the results of the project analyzing the performance of several scientific applications on several GPU-and SoC-based systems. We also describe the methodology we have used to measure energy performance and the tools we have implemented to monitor the power drained by applications while running.

Performance and Power Analysis of HPC Workloads on Heterogeneous Multi-Node Clusters

“…Various attempts to take advantage of mobile technology for increasing energy efficiency of HPC systems have also been taken in the recent past. The closest to our work are the EU Mont-Blanc project [16,17] and the COSA project [18,19], but several other examples can be found in the literature [20][21][22][23].…”

Editorial: Heterogeneous Computing for AI and Big Data in High Energy Physics

“…Heterogeneous Computing for AI and Big Data in High Energy Physics Heterogeneous computing denotes a scenario where different computing platforms are exploited for specific applications (Danovaro et al, 2014). While the demand for computational resources continues to grow with increasing need for querying and analyzing the volumes and rates of Big Data, energy efficiency is limiting the traditional approach to improve the compute capabilities of a data center by adding thousands of state-of-the-art x86 machines to an existing infrastructure in favor of adopting energy savvy devices (Cesini et al, 2017;D'Agostino et al, 2019). The result is that the computing nodes in data centers have different execution models, ranging from the traditional x68 architecture to GPUs, FPGAs (Papadimitriou et al, 2020) and other processor types like ARMs or more specialized processors as TPUs (Albrecht et al, 2019;Cass, 2019).…”

Hardware and Software Solutions for Energy-Efficient Computing in Scientific Programming

“…is was the goal of the Computing On SOC Architecture (COSA) project [46,47], an initiative funded by the Italian Institute for Nuclear Physics (INFN) between 2015 and 2018. In particular, the COSA project focused on assessing the energy consumption behavior of a wide set of state-of-the-art architectures using benchmarks and software widely used in many scientific applications.…”