Optimizations of a GPU accelerated heat conduction equation by a programming of CUDA Fortran from an analysis of a PTX file

The Fortran language has been commonly used for many kinds of scientific computation. In this paper, we focus on the solution of an unsteady heat conduction equation, which is one of the simplest problems for thermal dynamics. Recently, a GPU (graphics processing unit) has been enhanced with a Fortran programming language capability employing CUDA (compute unified device architecture), known as CUDA Fortran. We find that the speed performance of a system using an ordinary program coding of CUDA Fortran is lower than that of systems using a program coding of CUDA C. We also find that intermediate assembly files PTX (parallel thread execution) of the two languages are not coincident. Therefore, by comparing the PTX files from the two coding programs we could detect the bottleneck that causes the speed reduction. We propose three optimization techniques that can enable the calculated speeds using CUDA Fortran and CUDA C to be coincident. The optimizations can be performed by the Fortran language when improved by an analyzed PTX file. It is thus possible to improve the performance of CUDA Fortran by adding a correction to it, which happens to be at a programming language level.