Optimization strategies for geophysics models on manycore systems

Abstract

Many software mechanisms for geophysics exploration in oil and gas industries are based on wave propagation simulation. To perform such simulations, state-of-the-art high-performance computing architectures are employed, generating results faster with more accuracy at each generation. The software must evolve to support the new features of each design to keep performance scaling. Furthermore, it is important to understand the impact of each change applied to the software to improve the performance as most as possible. In this article, we propose several optimization strategies for a wave propagation model for six architectures: Intel Broadwell, Intel Haswell, Intel Knights Landing, Intel Knights Corner, NVIDIA Pascal, and NVIDIA Kepler. We focus on improving the cache memory usage, vectorization, load balancing, portability, and locality in the memory hierarchy. We analyze the hardware impact of the optimizations, providing insights of how each strategy can improve the performance. The results show that NVIDIA Pascal outperforms the other considered architectures by up to 8.5×.