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Performance and energy footprint assessment of FPGAs and GPUs on HPC systems using Astrophysics application
arXiv - CS - Performance Pub Date : 2020-03-06 , DOI: arxiv-2003.03283 David Goz, Georgios Ieronymakis, Vassilis Papaefstathiou, Nikolaos Dimou, Sara Bertocco, Francesco Simula, Antonio Ragagnin, Luca Tornatore, Igor Coretti, and Giuliano Taffoni
arXiv - CS - Performance Pub Date : 2020-03-06 , DOI: arxiv-2003.03283 David Goz, Georgios Ieronymakis, Vassilis Papaefstathiou, Nikolaos Dimou, Sara Bertocco, Francesco Simula, Antonio Ragagnin, Luca Tornatore, Igor Coretti, and Giuliano Taffoni
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:
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。
更新日期:2020-04-13
中文翻译:
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。