Improving SIMD Efficiency for Parallel Monte Carlo Light Transport on the GPU

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dc.contributor.authorAntwerpen, Dietger vanen_US
dc.contributor.editorCarsten Dachsbacher and William Mark and Jacopo Pantaleonien_US
dc.date.accessioned2016-02-18T11:01:48Z
dc.date.available2016-02-18T11:01:48Z
dc.date.issued2011en_US
dc.description.abstractMonte Carlo Light Transport algorithms such as Path Tracing (PT), Bi-Directional Path Tracing (BDPT) and Metropolis Light Transport (MLT) make use of random walks to sample light transport paths. When parallelizing these algorithms on the GPU the stochastic termination of random walks results in an uneven workload between samples, which reduces SIMD efficiency. In this paper we propose to combine stream compaction and sample regeneration to keep SIMD efficiency high during random walk construction, in spite of stochastic termination. Furthermore, for BDPT and MLT, we propose to evaluate all bidirectional connections of a sample in parallel in order to balance the workload between GPU threads and improve SIMD efficiency during sample evaluation. We present efficient parallel GPU-only implementations for PT, BDPT, and MLT in CUDA.We show that our GPU implementations outperform similarCPU implementations by an order of magnitude.en_US
dc.description.sectionheadersParallel Ray Tracingen_US
dc.description.seriesinformationEurographics/ ACM SIGGRAPH Symposium on High Performance Graphicsen_US
dc.identifier.doi10.1145/2018323.2018330en_US
dc.identifier.isbn978-1-4503-0896-0en_US
dc.identifier.issn2079-8687en_US
dc.identifier.pages41-50en_US
dc.identifier.urihttps://doi.org/10.1145/2018323.2018330en_US
dc.publisherACMen_US
dc.subjectI.3.7 [Computer Graphics]en_US
dc.subjectThree DimensionalGraphics and Realism Ray Tracingen_US
dc.subjectMonte Carlo Light Transporten_US
dc.subjectPath Tracingen_US
dc.subjectGPUen_US
dc.titleImproving SIMD Efficiency for Parallel Monte Carlo Light Transport on the GPUen_US
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High-Performance Graphics 2011