Acceleration of a Finite-Difference WENO Scheme for Large-Scale Simulations on Many-Core Architectures

Current trends on high performance computing are moving towards the deployment of several cores on the same chip of modern processors in order to achieve substantial execution speedup through the extraction of the potential fine-grain parallelism of applications. At the forefront of this trend we find nowadays the modern Graphics Processors Units (GPUs), which due to their simplistic design are able to encompass hundreds of independent processing units on a single chip in contrast to their respective CPUs, which at the moment include only a few cores on the same chip. In order to study the potential speedup of computationally intensive applications that utilize the many-core architecture of GPUs, this paper presents a highly accelerated implementation of the finite-difference weighted essentially non-oscillatory (WENO) scheme. This method is suitable for direct numerical simulations (DNS) large eddy simulations (LES) of compressible turbulence and requires large computing resources in order to achieve high Reynolds numbers. Our implementation targets on large-scale simulations using the CUDA parallel programming and constitues a paradigm of GPU’s applications in CFD. The results of the current implementation demonstrate that such a computationally intensive application could be highly accelerated running on the NVIDIA Tesla C1070 many-core GPU. I