Memory-Scalable GPU Spatial Hierarchy Construction
In: http://kunzhou.net/2009/mem-hierarchy-tr.pdf, 2011
Online
academicJournal
Zugriff:
We present two novel algorithms for constructing spatial hierarchies on GPUs. The first is for kd-trees that automatically balances between the level of parallelism and total memory usage by using a novel PBFS (partial breadth-first search) construction scheme. With this PBFS construction scheme, peak memory consumption can be efficiently controlled without costly CPU-GPU data transfer. We also develop memory allocation strategies to effectively limit memory fragmentation. The resulting algorithm scales well with GPU memory and constructs kd-trees of models with millions of triangles at interactive rates on GPUs with 1GB memory. Compared with existing algorithms, our algorithm is an order of magnitude more scalable for a given GPU memory bound. The second algorithm is for out-of-core BVH (bounding volume hierarchy) construction for very large scenes based on the PBFS construction order. At each iteration, all constructed nodes are dumped to the CPU memory, and the GPU memory is freed for the next iteration’s use. In this way, the algorithm is able to build trees that are too large to be stored in the GPU memory. Experiments show that our algorithm can construct BVHs for scenes with up to 20M triangles, several times larger than previous GPU algorithms.
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Memory-Scalable GPU Spatial Hierarchy Construction
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Autor/in / Beteiligte Person: | Hou, Qiming ; Sun, Xin ; Zhou, Kun ; Lauterbach, Christian ; Manocha, Dinesh ; Guo, Baining ; The Pennsylvania State University CiteSeerX Archives |
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Zeitschrift: | http://kunzhou.net/2009/mem-hierarchy-tr.pdf, 2011 |
Veröffentlichung: | 2011 |
Medientyp: | academicJournal |
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