OpenACC-based GPU Acceleration of a 3-D Unstructured Discontinuous Galerkin Method

Abstract

A GPU-accelerated discontinuous Galerkin (DG) method is presented for the solution of compressible flows on 3-D unstructured grids. The present work has employed two of the most attractive features in a new programming standard of parallel computing -OpenACC: 1) multi-platform/compiler support and 2) descriptive directive interface to upgrade a legacy CFD solver with the capacity of GPU computing, without significant extra cost in recoding, resulting in a highly portable and extensible GPU-accelerated code. In addition, a face renumbering/grouping scheme is proposed to overcome the "race condition" in facebased flux calculations that occurs on GPU vectorization. Performance of the developed double-precision solver is assessed for both simple and complex geometries. Speedup factors up to but not limited to 24× and 1.6× were achieved by comparing the measured computing time of the OpenACC program running on an NVIDIA Tesla K20c GPU to that of the equivalent MPI program running on one single core and full sixteen cores of an AMD Opteron-6128 CPU respectively, indicating a great potential to port more features of the underlying DG solver into the OpenACC framework.

Optimization of A Fine-grained BILU by CUDA Inter-block Synchronization

“…With the assistance of convenient development tools many software engineering issues of porting CFD codes can be trivially resolved, including data management and efficient data exchange between multiple GPGPUs. Our previous attempts of porting CFD codes to GPGPU using OpenACC and GPGPU-aware MPI implementations have proven the advantages on portability and maintainability of this approach [2,7]. More fundamental performance restrictions are encountered when porting implicit CFD solvers.…”

“…Recent development on hardware and software technologies of General Purpose Graphics Processing Unit (GPGPU) has greatly improved its potential for large-scale high performance computation (see, for example [1,2,3,4,5]). Maturing programming frameworks have allowed GPGPU algorithm designs to gain more popularity.…”

A fine-grained block ILU scheme on regular structures for GPGPUs

“…Recently, general-purpose graphics processing units (GPGPU) have attracted much attention as a promising technology for large-scale high performance computation (see, for example, [10][11][12][13][14][15][16]). A GPU, which is essentially a shared memory vector machine, has a potential to achieve one or two orders of magnitude of performance improvement for highly parallel algorithms.…”

Advanced Optimizations of An Implicit Navier-Stokes Solver on GPGPU

“…Many successful attempts have been reported in recent years (see, for example [1,2,3,4,5,6]). Although early attempts of utilizing GPGPU for CFD has been hampered by the heterogeneous nature of GPGPU hardware and complex programming tools, recent GPU technology has seen significant improvement on programming toolchain.…”

“…By allowing programmers to use a collection of compiler directives to specify loops and regions of their codes to be offloaded from a host CPU to GPGPU, this programming model offers a good balance between porting efforts and performance gain. As shown in [8,6], our previous attempts of porting existing CFD codes to GPGPU using OpenACC and MVAPICH2 [9] have proven the advantages on portability and maintainability of this approach.…”