混合CPU-GPU非结构化网格并行体渲染PC集群

Manuel Juliachs, T. Carrard, J. Nominé
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引用次数: 1

摘要

大规模数值模拟产生的数据集具有不断增长的规模和复杂性。特别是,在许多应用中会遇到非结构化网格。体绘制提供了一种有效分析此类数据集的方法。图形硬件的最新进展使得在GPU上实现高效的非结构化体绘制算法成为可能。然而,GPU架构的限制使得这些方法很难适用于并行实现,这对于以交互速度和高分辨率呈现非常大的数据集是必要的。许多以前的并行方法都集中在基于软件的算法上。在本文中,我们提出了一种混合对象空间/图像空间CPU- gpu分布式并行体绘制方法,利用CPU提供的灵活性,包括SIMD处理能力,并使用gpu执行深度排序和合成等重复任务。我们将不同阶段对整体渲染时间的影响作为节点数的函数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid CPU-GPU unstructured meshes parallel volume rendering on PC clusters
Large-scale numerical simulation produces datasets with ever-growing size and complexity. In particular, unstructured meshes are encountered in many applications. Volume rendering provides a way to efficiently analyze such datasets. Recent advances in graphics hardware have enabled the implementation of efficient unstructured volume rendering algorithms on the GPU. However, GPU architecture limitations make these methods difficultly amenable to a parallel implementation, which is necessary to render very large datasets at interactive speeds and high resolutions. Many previous parallel approaches have focused on softwarebased algorithms. In this paper, we present a hybrid object-space/image-space CPU-GPU distributed parallel volume rendering method, taking advantage of the flexibility afforded by the CPU, including SIMD processing capabilities, and using GPUs to perform repetitive tasks like depth-sorting and compositing. We present the impact of the different phases on the overall rendering time as a function of node number.
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