A Decompression Pipeline for Accelerating Out-of-Core Volume Rendering of Time-Varying Data

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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 GPU (graphics processing unit) 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 258×258×208 voxel data with 150 time steps. It also demonstrates an almost interactive rate of 8 fps for 512 × 512 × 295 voxel data with 411 time steps.

bOracle Corporation Japan/Osaka University
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