用GPU加速机对太阳日冕的大规模高Lundquist数简化MHD模拟
Large-Scale High-Lundquist Number Reduced MHD Simulations of the Solar Corona Using GPU Accelerated Machines
摘要：
我们最近进行了一项计算活动，调查模型的日冕加热在三维使用减少磁流体动力学（RMHD）。我们的代码是建立在一个传统的计划，使用伪谱方法，并使用MPI并行化。目前的调查需要非常长的时间集成使用高伦德奎斯特数，其中非常精细的电流层的形成挑战的决议，即使在大规模并行机上实现。我们在这里介绍一个端口的结果，以英伟达CUDA（计算统一设备架构）的硬件加速使用图形处理单元（GPU）。除了简要讨论我们的一般策略外，我们还将报告几台机器上的代码性能，这些机器跨越了各种硬件配置和功能。其中包括一个配备商用硬件的桌面工作站，一个配备四个Nvidia C2050 GPU的专用研究工作站，以及几个大型GPU加速的分布式内存机器：Lincoln/NCSA，Dirac/NERSC和Keeneland/NICS。
英文摘要：
We have recently carried out a computational campaign to investigate a model of coronal heating in three-dimensions using reduced magnetohydrodynamics (RMHD). Our code is built on a conventional scheme using the pseudo-spectral method, and is parallelized using MPI. The current investigation requires very long time integrations using high Lundquist numbers, where the formation of very fine current layers challenge the resolutions achievable even on massively parallel machines. We present here results of a port to Nvidia CUDA (Compute Unified Device Architecture) for hardware acceleration using graphics processing units (GPUs). In addition to a brief discussion of our general strategy, we will report code performance on several machines which span a variety of hardware configurations and capabilities. These include a desktop workstation with commodity hardware, a dedicated research workstation equipped with four Nvidia C2050 GPUs, as well as several large-scale GPU accelerated distributed memory machines: Lincoln/NCSA, Dirac/NERSC, and Keeneland/NICS.
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