The stochastic One-Dimensional Turbulence (ODT) model is used in combination with a Large Eddy Simulation (LES) approach in order to illustrate the potential of the fully coupled model (ODTLES) for highly turbulent flows. In this work, we use a new C++ implementation of the ODTLES code in order to analyze the computational performance in a classical incompressible turbulent channel flow problem. The parallelization potential of the model, as well as its physical and numerical consistency are evaluated and compared to Direct Numerical Simulations (DNSs). The numerical results show that the model is capable of reproducing a representative part of the DNS data at a cheaper computational cost. This advantage can be enhanced in the future by the implementation of a straightforward parallelization approach.
Pseudo-Uniform Memory Architectures hide the memory's throughput bottlenecks and the network's latency differences in order to provide near-peak average throughput for computations on large datasets. This obviates the need for application-level partitioning and load balancing between NUMA domains but the performance of cross-core communication still depends on the actual placement of the involved variables and cores, which can result in significant variation within applications and between application runs.
This paper analyses the pseudo-uniform memory latency on the Intel Xeon Phi Knights Corner processor, derives strategies for the optimised placement of important variables, and discusses the role of localised coordination in pUMA systems. For example, a basic cache line ping-pong benchmark showed a 3x speedup between adjacent cores. Therefore, pUMA systems combined with support for controlled placement of small datasets are an interesting option when processor-wide load balancing is difficult while localised coordination is feasible.