For example, a 1920x1080 scene using brute-force GI with 1024 rays per pixel needs to shoot a minimum of 2.1 billion rays! And this doesn't even include extra rays that might be needed for antialiasing, shadows, depth-of-field etc. The more rays we can send to the GPU in one go, the better the performance is. Because the GPU is a massively parallel processor, Redshift constantly builds lists of rays (the 'workload') and dispatches these to the GPU. Redshift also uses "geometry memory" and "texture cache" for polygons and textures respectively.Īdditionally, Redshift needs to allocate memory for rays. The first holds the scene's polygons while the second holds the textures. You might have seen other renderers refer to things like "dynamic geometry memory" or "texture cache". Some CPU renderers also do a similar kind of memory partitioning.
#How to find textures using sl cache viewer free
For this reason, Redshift has to partition free GPU memory between the different modules so that each one can operate within known limits which are defined at the beginning of each frame. Second, no robust methods exist for dynamically allocating GPU memory.
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There are main two issues at hand: First, the GPU has limited memory resources. One of the challenges with GPU programs is memory management. While these features are supported by most CPU biased renderers, getting them to work efficiently and predictably on the GPU was a significant challenge! Redshift supports a set of rendering features not found in other GPU renderers on the market such as point-based GI, flexible shader graphs, out-of-core texturing and out-of-core geometry.