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

TL;DR

This paper evaluates the performance of genetic algorithm-based airfoil optimization using panel methods on modern accelerators, demonstrating significant speedups with Intel Xeon Phi 7120 and NVIDIA K80 compared to CPU implementations.

Contribution

The paper presents optimized implementations of an airfoil optimization algorithm on CPU, Xeon Phi, and GPU, providing a comparative performance analysis.

Findings

Speedup of approximately 2.5 with Intel Xeon Phi 7120.

Speedup between 3.4 and 3.8 with NVIDIA K80.

Discussion of implementation differences and similarities.

Abstract

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.