This article presents a solution to path tracing of massive scenes on multiple GPUs. Our approach analyzes the memory access pattern of a path tracer and defines how the scene data should be distributed across up to 16 GPUs with minimal effect on performance. The key concept is that the parts of the scene that have the highest amount of memory accesses are replicated on all GPUs. We propose two methods for maximizing the performance of path tracing when working with partially distributed scene data. Both methods work on the memory management level and therefore path tracer data structures do not have to be redesigned, making our approach applicable to other path tracers with only minor changes in their code. As a proof of concept, we have enhanced the open-source Blender Cycles path tracer. The approach was validated on scenes of sizes up to 169 GB. We show that only 1–5% of the scene data needs to be replicated to all machines for such large scenes. On smaller scenes we have verified that the performance is very close to rendering a fully replicated scene. In terms of scalability we have achieved a parallel efficiency of over 94% using up to 16 GPUs.

中文翻译：

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大规模场景的 GPU 加速路径跟踪

本文提出了一种在多 GPU 上对海量场景进行路径追踪的解决方案。我们的方法分析了路径跟踪器的内存访问模式，并定义了场景数据应如何分布在多达 16 个 GPU 上，同时对性能的影响最小。关键概念是在所有 GPU 上复制具有最高内存访问量的场景部分。我们提出了两种在处理部分分布式场景数据时最大化路径跟踪性能的方法。这两种方法都适用于内存管理级别，因此不必重新设计路径跟踪器数据结构，使我们的方法适用于其他路径跟踪器，只需对其代码进行微小更改。作为概念验证，我们增强了开源 Blender Cycles 路径跟踪器。该方法已在最大 169 GB 的场景中得到验证。我们表明，对于如此大的场景，只需将 1-5% 的场景数据复制到所有机器上。在较小的场景中，我们已经验证了性能非常接近于渲染完全复制的场景。在可扩展性方面，我们使用多达 16 个 GPU 实现了超过 94% 的并行效率。