CPU-GPU Multithreaded Programming Model: Application to the Path Tracing with Next Event Estimation Algorithm

Today’s hardware includes powerful devices such as graphics process units (GPU) that are not always used to their maximum capacities. Our main goal is to take advantage of these unused resources. To achieve this, we abstract GPUs as SIMD streaming coprocessors and use them within the framework of a multithreaded parallel model. Thus we aim to use all the computing power of a modern PC for speeding up a global illumination simulation software. The global illumination of a virtual scene can be estimated with stochastic methods such as Path Tracing. These methods however remain costly in terms of rendering time, because of the high sampling required to produce good quality frames. The most part of the rendering time is spent performing intersections tests between rays and triangles. We propose to speed up the rendering of a frame, by using all the available CPUs and GPUs. Our work is based on the ray engine developed by Carr et al. for ray tracing, and is mapped to our parallel programming model.