GPUs have been used for years to display 3D configurators, but their use to display final results is only now getting on in years. Many popular rendering packages have GPU-based alternatives to their flagship software. Chaos Group produces a GPU-based version of V-Ray called V-Ray RT. With iRay, Nvidia has created an alternative to Mental Ray. Standalone GPU renderers like Redshift, Octane and Furryball are also becoming more and more popular.
GPU rendering, which relies on memory rather than processor speed, can be much faster than normal CPU rendering. The speed increase is due to how different processors process jobs. The main processor on a motherboard is good at performing a few difficult calculations at once. Imagine the CPU as the factory manager making thoughtful, difficult decisions.
A GPU, on the other hand, is more like a whole group of people in the factory. Although they can’t do the same kind of calculations, they can do many, many more tasks at once without being overwhelmed. Many rendering tasks are the kind of repetitive, brute force functions in which GPUs are good. You can also stack multiple GPUs in one computer. This all means that GPU systems can often render much, much faster.
There’s also a big advantage that comes long before the final output is created. GPU rendering is so fast that it can often provide real-time feedback during work. You no longer need to drink a cup of coffee while your preview is rendered. You can see how material and light changes take place before your eyes. So why don’t we all just switch to GPU rendering and go home early? It’s not that easy. GPU-based renderers aren’t as sophisticated as their older GPU-based cousins. Developers are constantly adding new features, but they still don’t support all the tools 3D artists expect from a rendering solution.
Things like displacement, hair and volume measurement are often missing in GPU-based engines. The biggest problem with GPU rendering can be the way GPUs process a scene.
The all-in-one feature of GPU rendering requires that an entire 3D scene be loaded into memory to work. Large scenes with tons of polygons and many high-resolution textures will simply not work for some GPU-based solutions.
It’s also a learning curve. Many GPU renderers require some materials, shaders and lighting. Thus, scenes set up for GPU-based rendering cannot simply be switched to a GPU renderer, even if the same company produces the software. 3D artists must decide at the beginning of the project which workflow they want to use.
Will GPU rendering ever connect to CPU-based software? Will it dominate the 3D industry? Time will tell. In the meantime, the best way to render quickly and still enjoy the advanced features of CPU rendering is to use a cloud solution like Rayvision.
We hope this article has given you an overview of the future development of GPU rendering. If you have any comments or questions, please feel free to contact our experts in our forum.
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