Generalized GPU Acceleration for Applications Employing Finite-Volume Methods

Abstract

Scientific HPC applications are increasingly ported to GPUs to benefit from both the high throughput and the powerful computing capacity. Many of these applications, such as atmospheric modeling and hydraulic erosion simulation, are adopting the finite volume method (FVM) as the solver algorithm. However, the communication components inside these applications generally lead to a low flop-to-byte ratio and an inefficient utilization of GPU resources. This paper aims at optimizing FVM solver based on the structured mesh. Besides a high-level overview of the finite-volume method as well as its basic optimizations on modern GPU platforms, we further present two generalized tuning techniques including an explicit cache mechanism as well as an inner-thread rescheduling method that tries to achieve a suitable mapping between the algorithm feature and the platform architecture. To the end, we demonstrate the impact of our generalized optimization methods in two typical atmospheric dynamic kernels (Euler and SWE) based on four mainstream GPU platforms. According to the experimental results of Tesla K80, speedups of 24.4x for SWE and 31.5x for Euler could be achieved over a 12-core Intel E5-2697 CPU, which is a great promotion compared with its original speedup (18x and 15.47x) without applying these two methods.