Technical Section: A decompression pipeline for accelerating out-of-core volume rendering of time-varying data (ACM)

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This paper presents a decompression pipeline capable of accelerating out-of-core volume rendering of time-varying scalar data. Our pipeline is based on a two-stage compression method that cooperatively uses the CPU and the graphics processing unit (GPU) to transfer compressed data entirely from the storage device to the video memory. This method combines two different compression algorithms, namely packed volume texture compression (PVTC) and Lempel-Ziv-Oberhumer (LZO) compression, allowing us to exploit both temporal and spatial coherence in time-varying data. Furthermore, it achieves fast decompression by taking architectural advantages of each processing unit: a hardware component on the GPU and a large cache on the CPU, each suited to decompress PVTC and LZO encoded data, respectively. We also integrate the method with a thread-based pipeline mechanism to increase the data throughput by overlapping data loading, data decompression, and rendering stages. Our pipelined renderer runs on a quad-core PC and achieves a video rate of 41 frames per second (fps) in average for 258x258x208 voxel data with 150 time steps. It also demonstrates an almost interactive rate of 8fps for 512x512x295 voxel data with 411 time steps.



Paper available at ACM.

Graduate School of Information Science and Technology, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan and Oracle Corporation Japan, 4-1 Kioi, Chiyoda, Tokyo 102-0094, Japan
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