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A Novel Shared-Memory Thread-Pool Implementation for Hybrid Parallel CFD Solvers

机译:混合并行CFD解决方案的新型共享内存线程池实现

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The Computational Fluid Dynamics (CFD) solver TAU for unstructured grids is widely used in the European aerospace industry. TAU runs on High-Performance Computing (HPC) clusters with several thousands of cores using MPI-based domain decomposition. In order to make more efficient use of current multi-core CPUs and to prepare TAU for the many-core era, a shared-memory parallelization has been added to one of TAU's solver to obtain a hybrid parallelization: MPI-based domain decomposition plus multi-threaded processing of a domain. For the edge-based solver considered, a simple loop-based approach via OpenMP FOR directives would - due to the Amdahl trap - not deliver the required speed-up. A more sophisticated, thread-pool-based shared-memory parallelization has been developed which allows for a relaxed thread synchronization with automatic and dynamic load balancing. In this paper we describe the concept behind this shared-memory parallelization, we explain how the multi-threaded computation of a domain works. Some details of its implementation in TAU as well as some first performance results are presented. We emphasize that the concept is not TAU-specific. Actually, this design pattern appears to be very generic and may well be applied to other grid/mesh/graph-based codes.
机译:用于非结构化网格的计算流体动力学(CFD)求解器TAU在欧洲航空航天工业中得到广泛使用。 TAU使用基于MPI的域分解在具有数千个内核的高性能计算(HPC)群集上运行。为了更有效地利用当前的多核CPU并为多核时代做好TAU的准备,已将共享内存并行化添加到TAU的一个求解器中以获得混合并行化:基于MPI的域分解加多域的线程处理。对于考虑的基于边缘的求解器,由于使用了Amdahl陷阱,因此通过OpenMP FOR指令进行的基于循环的简单方法将无法实现所需的加速。已经开发了更加复杂的基于线程池的共享内存并行化,该并行化允许通过自动和动态负载平衡实现轻松的线程同步。在本文中,我们描述了共享内存并行化背后的概念,并解释了域的多线程计算是如何工作的。本文介绍了其在TAU中实施的一些细节以及一些初步的性能结果。我们强调,该概念不是特定于TAU的。实际上,这种设计模式似乎非常通用,可以很好地应用于其他基于网格/网格/图形的代码。

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