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High Performance Computational Hydrodynamic Simulations: UPC Parallel Architecture as a Future Alternative

机译:高性能计算流体力学仿真:UPC并行架构作为未来的替代方案

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Developments in high performance computing (HPC) has today transformed the manner of how computational hydrodynamic (CHD) simulations are performed. Till now, the message passing interface (MPI) remains the common parallelism architecture and has been adopted widely in CHD simulations. However, its bottleneck problem remains for some large-scale simulation cases due to delays during message passing whereby the total communication time may exceed the total simulation runtime with an increasing number of computer processers. In this study, we utilise an alternative parallelism architecture, known as PGAS-UPC, to develop our own UPC-CHD model with a 2-step explicit scheme from the Lax-Wendroff family of predictors-correctors. The model is evaluated on three incompressible, adiabatic viscous 2D flow cases having moderate flow velocities. Model validation is achieved by the reasonably good agreement between the predicted and respective analytical values. We then compare the computational performance between UPC-CHD and that of MPI in its base design in a SGI UV-2000 server till 100 processers maximum in this study. The former achieves a near 1:1 speedup which demonstrates its efficiency potential for very large-scale CHD simulations, while the later experiences slowdown at some point. Extension of UPC-CHD remains our main objective which can be achieved by the following additions: (a) inclusions of other numerical schemes to accommodate for other types of fluid simulations, and (b) coupling UPC-CHD with Amazon Web Service (AWS) to further exploit its parallelism efficiency as a viable alternative.
机译:如今,高性能计算(HPC)的发展已经改变了如何执行计算流体力学(CHD)仿真的方式。到目前为止,消息传递接口(MPI)仍然是通用的并行性体系结构,并已在CHD模拟中被广泛采用。但是,由于消息传递期间的延迟,对于某些大规模的模拟情况,其瓶颈问题仍然存在,因此随着计算机处理程序数量的增加,总的通信时间可能会超过总的模拟运行时间。在这项研究中,我们利用另一种称为PGAS-UPC的并行架构,使用Lax-Wendroff预测器-校正器系列的两步显式方案来开发我们自己的UPC-CHD模型。在具有中等流速的三个不可压缩的绝热粘性2D流动案例上对该模型进行了评估。通过在预测值和相应分析值之间达成合理的良好协议来实现模型验证。然后,我们在SGI UV-2000服务器的基础设计中比较了UPC-CHD和MPI在其基础设计中的计算性能,直到本研究最多使用100个处理器。前者实现了接近1:1的加速,这证明了其在超大型CHD模拟中的效率潜力,而后者则在某点上有所降低。扩展UPC-CHD仍然是我们的主要目标,可以通过以下添加操作来实现:(a)包含其他数值方案以适应其他类型的流体模拟,以及(b)将UPC-CHD与Amazon Web Service(AWS)耦合进一步利用其并行性效率作为可行的替代方案。

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