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Representing and Scheduling Procedural Generation using Operator Graphs
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DOI：10.1145/2980179.2980227.png)
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Abstract
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In this paper, we present the concept of operator graph scheduling for high performance procedural generation on the graphics processing unit (GPU). The operator graph forms an intermediate representation that describes all possible operations and objects that can arise during a specific procedural generation. While previous methods have focused on parallelizing a specific procedural approach, the operator graph is applicable to all procedural generation methods that can be described by a graph, such as L-systems, shape grammars, or stack based generation methods. Using the operator graph, we show that all partitions of the graph correspond to possible ways of scheduling a procedural generation on the GPU, including the scheduling strategies of previous work. As the space of possible partitions is very large, we describe three search heuristics, aiding an optimizer in finding the fastest valid schedule for any given operator graph. The best partitions found by our optimizer increase performance of 8 to 30x over the previous state of the art in GPU shape grammar and L-system generation.
Keywords:
Procedural Generation
Operator Graph
GPU
Auto-tuner
Dynamic Scheduling
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