Performance analysis for GPU-based ray-triangle algorithms

Several algorithms have been proposed during the past years to solve the ray-triangle intersection test. In this paper we collect the most prominent solutions and describe how to parallelize them on modern programmable graphics processing units (GPUs) by means of NVIDIA CUDA. This paper also provides a comprehensive performance analysis based on several optional features and optimizations (such as back-face culling and the use of pre-computed values) that allowed us to determine the influence of each factor on the performance. Finally, we analyze the architecture of the GPU and its impact on the parallel implementation of each method, as well as the approach used to achieve a high-performance fine-grained parallel computation on the ray-triangle test.

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