Master Thesis - Computer Graphics and Visualization - TU Delft

Hybrid Tracer 6.3 Results
6.3.3 Conclusion
On our test platform, the performance of the hybrid architecture is bound by the system
memory bandwidth and is therefore not very scalable. Figure 6.5 gives an indication of the
enormous amount of GPU performance that is wasted. Because the sampler is only implemented
on the CPU, work balancing is difficult when system memory bandwidth becomes
the bottleneck. This problem could be targeted by using much faster system memory. However,
advancing a large stream of generic samplers remains cache unfriendly and therefore
does not fit the CPU memory architecture very well. In contrast, the GPU is designed for
processing large streams of data in parallel. Therefore, in the remainder of this thesis we
will try to implement the sampler on the GPU, moving the complete rendering algorithm
to the GPU and effectively eliminating the CPU and PCIe as possible bottlenecks. As an
added advantage, this makes it much easier to fully utilize all available GPU’s in the system
with multiple GPU’s.
In the following chapters, we will implement variations of several well known unbiased
samplers on the GPU. The work flow of these samplers will closely resemble the generic
sampler framework of iteratively advancing samplers and traversing corresponding output
rays.
Figure 6.6: Sibenik cathedral rendered with hybrid PT.
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