New challenges in Astronomy and Astrophysics (AA) are urging the need for a large number of exceptionally computationally intensive simulations. "Exascale" (and beyond) computational facilities are mandatory to address the size of theoretical problems and data coming from the new generation of observational facilities in AA. Currently, the High Performance Computing (HPC) sector is undergoing a profound phase of innovation, in which the primary challenge to the achievement of the "Exascale" is the power-consumption. The goal of this work is to give some insights about performance and energy footprint of contemporary architectures for a real astrophysical application in an HPC context. We use a state-of-the-art N-body application that we re-engineered and optimized to exploit the heterogeneous underlying hardware fully. We quantitatively evaluate the impact of computation on energy consumption when running on four different platforms. Two of them represent the current HPC systems (Intel-based and equipped with NVIDIA GPUs), one is a micro-cluster based on ARM-MPSoC, and one is a "prototype towards Exascale" equipped with ARM-MPSoCs tightly coupled with FPGAs. We investigate the behavior of the different devices where the high-end GPUs excel in terms of time-to-solution while MPSoC-FPGA systems outperform GPUs in power consumption. Our experience reveals that considering FPGAs for computationally intensive application seems very promising, as their performance is improving to meet the requirements of scientific applications. This work can be a reference for future platforms development for astrophysics applications where computationally intensive calculations are required.

中文翻译：

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使用天体物理学应用程序对 HPC 系统上的 FPGA 和 GPU 进行性能和能源足迹评估

天文学和天体物理学 (AA) 的新挑战促使需要进行大量计算量极大的模拟。“Exascale”（及以上）计算设施对于解决来自 AA 新一代观测设施的理论问题和数据的规模是强制性的。当前，高性能计算（HPC）行业正处于深度创新阶段，实现“百亿亿级”的首要挑战是功耗。这项工作的目标是为 HPC 环境中的真实天体物理应用提供有关当代架构的性能和能源足迹的一些见解。我们使用我们重新设计和优化的最先进的 N 体应用程序来充分利用异构底层硬件。我们定量评估了在四种不同平台上运行时计算对能耗的影响。其中两个代表当前的HPC系统（基于Intel并配备NVIDIA GPU），一个是基于ARM-MPSoC的微集群，一个是配备与FPGA紧密耦合的ARM-MPSoC的“Exascale原型”。我们调查了不同设备的行为，其中高端 GPU 在解决时间方面表现出色，而 MPSoC-FPGA 系统在功耗方面优于 GPU。我们的经验表明，将 FPGA 用于计算密集型应用似乎非常有希望，因为它们的性能正在提高以满足科学应用的要求。