Direct N-body application on low-power and energy-efficient parallel architectures

TL;DR

This paper evaluates the energy efficiency of ARM MPSoC platforms, including CPUs, GPUs, and FPGAs, for direct N-body simulations, highlighting FPGA's potential for energy-efficient HPC applications.

Contribution

It provides a comparative analysis of performance and energy consumption across different hardware architectures for N-body simulations, emphasizing FPGA's promising role.

Findings

FPGAs can accelerate application kernels effectively.

FPGAs are a promising alternative to traditional HPC technologies.

Energy efficiency varies significantly across hardware types.

Abstract

The aim of this work is to quantitatively evaluate the impact of computation on the energy consumption on ARM MPSoC platforms, exploiting CPUs, embedded GPUs and FPGAs. One of them possibly represents the future of High Performance Computing systems: a prototype of an Exascale supercomputer. Performance and energy measurements are made using a state-of-the-art direct $N$-body code from the astrophysical domain. We provide a comparison of the time-to-solution and energy delay product metrics, for different software configurations. We have shown that FPGA technologies can be used for application kernel acceleration and are emerging as a promising alternative to "traditional" technologies for HPC, which purely focus on peak-performance than on power-efficiency.