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Parallel Reyes-style Adaptive Subdivision with Bounded Memory Usage


  Recent advances in graphics hardware have made it a desirable goal to implement the Reyes algorithm on current graphics cards.  One key component in this algorithm is the bound-and-split phase, where surface patches are recursively split until they are smaller than a given screen-space bound.  While this operation has been successfully parallelized for execution on the GPU using a breadth-first traversal, the resulting implementations are limited by their unpredictable worst-case memory consumption and high global memory bandwidth utilization.  In this paper, we propose an alternate strategy that allows limiting the amount of necessary memory by controlling the number of assigned worker threads.  The result is an implementation that scales to the performance of the breadth-first approach while offering three advantages: significantly decreased memory usage, a smooth and predictable tradeoff between memory usage and performance, and increased locality for surface processing.  This allows us to render scenes that would require too much memory to be processed by the breadth-first method.

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