使用天体物理学应用程序评估HPC系统上FPGA和GPU的性能和能量足迹
Performance and energy footprint assessment of FPGAs and GPUs on HPC systems using Astrophysics application
摘要：
天文学和天体物理学（AA）的新挑战迫切需要大量的计算密集型模拟。“Exascale”（及以上）计算设施是强制性的，以解决理论问题的规模和数据来自新一代的观测设施在AA。当前，高性能计算（HPC）领域正经历一个深刻的创新阶段，其中实现“Exascale”的主要挑战是功耗。这项工作的目标是提供一些见解的性能和能源足迹的当代架构的一个真正的天体物理应用在HPC的上下文中。我们使用最先进的N体应用程序，我们重新设计和优化，充分利用异构的底层硬件。我们定量地评估了在四个不同平台上运行时计算对能耗的影响。其中两个代表了当前的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ARM-MPSoC与FPGA紧密耦合的“迈向Exascale的原型”。我们研究了不同设备的行为，其中高端GPU在解决方案时间方面表现出色，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前途，因为它们的性能正在提高，以满足科学应用的要求。这项工作可以为未来的天体物理应用平台的开发提供参考，其中需要计算密集型的计算。
英文摘要：
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.
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