In the last decade, computational power, memory bandwidth and programmability capabilities
of graphics processing units (GPU) have rapidly evolved. Therefore, many researches
have been performed to use GPUs in advanced graphics rendering. Because of its high degree
of parallelism, ray tracing has been one of the rst algorithms studied on GPUs. However, the
rendering of large scenes with ray tracing can easily exceed the GPU' / s memory capacity. The
algorithm proposed in this work uses a data parallel approach where the scene is partitioned
and assigned to CPU-GPU couples in a cluster to overcome this problem. Our algorithm
focuses on ray casting which is a special case of ray tracing mainly used in visualization of
volumetric data. CPUs are pretty ecient in ow control and branching while GPUs are
very fast performing intense oating point operations. Using these facts, the GPUs in the
cluster are assigned the task of performing ray casting while the CPUs are responsible for
traversing the rays. In the end, we were able to visualize large scenes successfully by utilizing
CPU-GPU couples eectively and observed that the performance is highly dependent on the
viewing angle as a result of load imbalance.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12609494/index.pdf |
| Date | 01 June 2008 |
| Creators | Topcu, Tumer |
| Contributors | Isler, Veysi |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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