Lattice Boltzmann Simulations at Petascale on Multi-GPU Systems with Asynchronous Data Transfer and Strictly Enforced Memory Read Alignment

F. Robertsen, J. Westerholm, K. Mattila
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引用次数: 8

Abstract

The lattice Boltzmann method is a well-established numerical approach for complex fluid flow simulations. Recently general-purpose graphics processing units have become accessible as high-performance computing resources at large-scale. We report on implementing a lattice Boltzmann solver for multi-GPU systems that achieves 0.69 PFLOPS performance on 16384 GPUs. In addition to optimizing the data layout on the GPUs and eliminating the halo sites, we make use of the possibility to overlap data transfer between the host CPU and the device GPU with computing on the GPU. We simulate flow in porous media and measure both strong and weak scaling performance with the emphasis being on a large scale simulation using realistic input data.
具有异步数据传输和严格强制内存读取对齐的千兆级多gpu系统上的晶格玻尔兹曼模拟
晶格玻尔兹曼方法是一种成熟的模拟复杂流体流动的数值方法。最近,通用图形处理单元已经成为大规模的高性能计算资源。我们报告了实现多gpu系统的晶格玻尔兹曼解算器,在16384个gpu上实现了0.69 PFLOPS的性能。除了优化GPU上的数据布局和消除晕点之外,我们还利用了主机CPU和设备GPU之间的数据传输与GPU上的计算重叠的可能性。我们模拟了多孔介质中的流动,并测量了强和弱尺度性能,重点是使用真实的输入数据进行大规模模拟。
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