Implementation and Benchmarking of Two-Dimensional Vortex Interactions on a Graphics Processing Unit

Chabalko, Christopher; Balachandran, Balakumar

doi:10.2514/1.i010141

Cited by 4 publications

(2 citation statements)

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“…As such it can potentially profit significantly from accelerators such as graphic processing units (GPUs) or the Intel Xeon Phi. In fact, some papers have been published that implement panel methods on GPUs (see, for example, the work conducted in [9][10][11][12]). However, most of the literature focuses on the three dimensional case.…”

Section: Introductionmentioning

confidence: 99%

“…As we will see in section 3 this is not true for the two-dimensional problem. In addition, speedups between one and two orders of magnitude are routinely reported [9,10,12,13]. However, since the hardware characteristics of the central processing unit (CPU) and the graphic processing unit (GPU) do not admit such a large difference in performance, it has to be concluded that the performance on the GPU has been compared to a CPU implementation that is not very well optimized.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the Intel Xeon Phi 7120 and NVIDIA K80 as accelerators for two-dimensional panel codes

Einkemmer

2017

PLoS ONE

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To optimize the geometry of airfoils for a specific application is an important engineering problem. In this context genetic algorithms have enjoyed some success as they are able to explore the search space without getting stuck in local optima. However, these algorithms require the computation of aerodynamic properties for a significant number of airfoil geometries. Consequently, for low-speed aerodynamics, panel methods are most often used as the inner solver. In this paper we evaluate the performance of such an optimization algorithm on modern accelerators (more specifically, the Intel Xeon Phi 7120 and the NVIDIA K80). For that purpose, we have implemented an optimized version of the algorithm on the CPU and Xeon Phi (based on OpenMP, vectorization, and the Intel MKL library) and on the GPU (based on CUDA and the MAGMA library). We present timing results for all codes and discuss the similarities and differences between the three implementations. Overall, we observe a speedup of approximately 2.5 for adding an Intel Xeon Phi 7120 to a dual socket workstation and a speedup between 3.4 and 3.8 for adding a NVIDIA K80 to a dual socket workstation.

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Section: Introductionmentioning

confidence: 99%