Direct $N$-body code on low-power embedded ARM GPUs. (arXiv:1901.08532v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Goz_D/0/1/0/all/0/1">David Goz</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bertocco_S/0/1/0/all/0/1">Sara Bertocco</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Tornatore_L/0/1/0/all/0/1">Luca Tornatore</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Taffoni_G/0/1/0/all/0/1">Giuliano Taffoni</a>
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.
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.
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