Towards Performance Portability of an Oil and Gas Application on Heterogeneous Architectures

With the widespread use of graphic processing units (GPUs) from different vendors in oil and gas companies, it has become increasingly important to achieve code portability. This allows for evaluating performance across diverse GPU vendors, enabling informed decisions. This capability enables companies to assess hardware solutions based on cost, performance, or energy efficiency, promoting competition and potentially driving innovation and cost reduction in GPU technologies. With that in mind, we address the challenge of achieving performance portability for Fletcher, an anisotropic wave propagation modeling application across various GPU architectures from NVIDIA and AMD. Hence, our first contribution is the parallel implementation of Fletcher on GPUs using eleven variations of portable programming models, including HIP, CUDA, Kokkos, RAJA, and OpenMP Target. Our second contribution is a comprehensive evaluation of these implementations across five generations of GPU from NVIDIA and AMD, assessing performance, performance-portability, and power-performance efficiency. Through an extensive set of experiments, we demonstrate that HIP outperforms the assessed programming models in terms of performance portability across all evaluated GPUs. Specifically, it performs 7.9% better than Kokkos, 8.8% better than RAJA, and 67.8% better than OpenMP Target. We also show that while the automatic translation of CUDA to HIP code allows for execution on AMD GPUs, optimizing for high performance is necessary. Additionally, we show that the programming models' impact on Fletcher's performance is heavily influenced by the size of the GPU's L2 cache, especially given that this is a memory-intensive application.