# Direct $N$-body code on low-power embedded ARM GPUs

## TL;DR

This paper demonstrates that embedded GPUs on low-power ARM-based SoCs can efficiently accelerate direct N-body simulations, offering a promising energy-efficient approach for exascale supercomputing.

## Contribution

It presents a re-engineered direct N-body code optimized for heterogeneous ARM SoC platforms with embedded GPUs, highlighting their performance and energy efficiency benefits.

## Key findings

- Embedded GPUs effectively accelerate N-body simulations.
- Performance comparable to traditional HPC systems.
- Energy efficiency is significantly improved.

## Abstract

This work arises on the environment of the ExaNeSt project aiming at design and development of an exascale ready supercomputer with low energy consumption profile but able to support the most demanding scientific and technical applications. The ExaNeSt compute unit consists of densely-packed low-power 64-bit ARM processors, embedded within Xilinx FPGA SoCs. SoC boards are heterogeneous architecture where computing power is supplied both by CPUs and GPUs, and are emerging as a possible low-power and low-cost alternative to clusters based on traditional CPUs. A state-of-the-art direct $N$-body code suitable for astrophysical simulations has been re-engineered in order to exploit SoC heterogeneous platforms based on ARM CPUs and embedded GPUs. Performance tests show that embedded GPUs can be effectively used to accelerate real-life scientific calculations, and that are promising also because of their energy efficiency, which is a crucial design in future exascale platforms.