Document Type thesis Author Name Chang, Chen Hao Jason Email Address ninjitaru at gmail.com URN etd-010807-140122 Title The Study of Energy Consumption of Acceleration Structures for Dynamic CPU and GPU Ray Tracing Degree MS Department Computer Science Advisors Emmanuel Agu, Advisor Robert W. Lindeman, Co-Advisor Mark Claypool, Reader Michael Gennert, Department Head Keywords Battery Energy Ray Tracing GPU Dynamic Scene Acceleration Structure Date of Presentation/Defense 2007-01-19 Availability unrestricted
Battery life has been the slowest growing resource on mobile systems for several decades. Although much work has been done on designing new chips and peripherals that use less energy, there has not been much work on reducing energy consumption by removing energy intensive tasks from graphics algorithms. In our work, we focus on energy consumption of the ray tracing task because it is a resource-intensive, global-illumination algorithm. We focus our effort on ray tracing dynamic scenes, thus we concentrate on identifying the major elements determining the energy consumption of acceleration structures. We believe acceleration structures are critical in reducing energy consumption because they need to be built inexpensively, but must also be complex enough to boost rendering speed.
We conducted tests on a Pentium 1.6 GHz laptop with GeForce Go 6800 GPU. In our experiments, we investigated various elements that modify the acceleration structure build algorithm, and we compared the energy usage of CPU and GPU rendering with different acceleration structures. Furthermore, the energy per frame when ray tracing dynamic scenes was gathered and compared to identify the best acceleration structure that provides a good balance between building energy consumption and rendering energy consumption.
We found the bounding volume hierarchy to be the best acceleration structure when rendering dynamic scenes with the GPU on our test system. A bounding volume hierarchy is not the most inexpensive structure to build, but it can be rendered cheaply on the GPU while introducing acceptable energy overhead when rebuilding. In addition, we found the fastest algorithm was also the most inexpensive in terms of energy consumption. We propose an energy model based on this finding.
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