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

机译:CPU-GPU多线程编程模型:应用于下一个事件估计算法的路径跟踪

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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.
机译:今天的硬件包括强大的设备,例如图形处理单元(GPU)并不总是用于其最大容量。我们的主要目标是利用这些未使用的资源。为实现这一目标,我们将GPU作为SIMD流式协处理器,并在多线程并行模型的框架内使用它们。因此,我们的目标是使用现代PC的所有计算能力来加速全局照明仿真软件。可以用诸如路径跟踪的随机方法估算虚拟场景的全局照明。然而,由于生产良好质量框架所需的高采样,这些方法在渲染时间方面仍然昂贵。渲染时间的大部分是在光线和三角形之间进行交叉测试。我们建议通过使用所有可用的CPU和GPU来加快框架的渲染。我们的作品基于Carr等人开发的Ray发动机。对于光线跟踪,并映射到我们的并联编程模型。

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