With the current trend of using co-processors for accelerating computations, we are presented with architectures and corresponding programming languages. The inability to predict lasting languages and architectures has led to the development of distinct languages and standards. This thesis details my work on occa, a unified threading language presented as a portable solution to hardware-accelerated coding that combines aspects of OpenMP, OpenCL, and CUDA. With the similarities between OpenMP, OpenCL and CUDA, I present a macro-based approach on a unified kernel language that currently encompasses OpenMP, OpenCL and CUDA. Along with kernel generation, occa includes an API (application programming interface) which serves as a wrapper on the three multi-threading languages. The back-end on occa dynamically compiles and loads function objects for a flexible run-time environment to use different hardware architectures.

Computational results using a spectrum of methods, namely finite difference, spectral element and discontinuous Galerkin methods, utilizing occa are shown to deliver portable high performance on different architectures and platforms. The finite difference method chapter reverse engineers optimized code written in CUDA and used in industry, discusses distinct features available in CUDA and compares occa implementations using different optimization techniques. The spectral element method and discontinuous Galerkin methods are derived from two projects I worked on during my studies: gNek, a distributed high-order spectral element method (SEM) implementation for the incompressible Navier-Stokes equations, and RiDG, equipped with discontinuous Galerkin (DG) to simulate acoustic wave equations under different assumptions in the material anisotropies. The parallel algorithms used to achieve high parallelization for GPU acceleration are discussed in both, gNek and RiDG, together with performance results.