GPU Friendly Data Compression - GPU Technology Conference

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Decoding at a glance• Fetch Level 0: 2x2 pixels for front and back slice• Interpolate to desired slice• Expand to Level 1:32x32, fetch detail.• Interpolate detail to desired slice– Can be done on Codebook entries, not on images• Repeat for Levels 2 & 3: 128x128 & 512x512• Finally, add detail for desired sliceKing Abdullah University of Science and Technology36
Round-up• Vector Quantization used here as lossy backend• 2D hierarchical prediction• linear prediction between slices• Decodes slices into a GPU cache before rendering– To ensure correct filtering between VQ blocks• Decoding speed (GTX 480 using shared memory)– 0.58ms per slice– Entire stack can recycle data and is significantly faster– Close to 50% for expand and VQ decoding each.King Abdullah University of Science and Technology37
Page 3: Motivation• There are tons of met
Page 6 and 7: Block-Truncation CodesDXT, PVRTC, .
Page 8 and 9: YCoCg DXT• 2 neat contributions t
Page 10 and 11: Treib et al. „Turbulence Visualiz
Page 12 and 13: Basic outline for color images• C
Page 14 and 15: Results on GeForce GTX 580• Throu
Page 16 and 17: Efficient Out-of-Core Terrain Rende
Page 28 and 29: Hierarchical Vector Quantization•
Page 30 and 31: Vector Quantization - Decoder (GPU)
Page 32 and 33: Hierarchical Vector Quantization -
Page 36 and 37: Take-AwayConfocal MicroscopyKing Ab
Page 38 and 39: Custom Compression• The Tools:- K
Page 40 and 41: Custom Compression - The idea for c
Page 42 and 43: Decoding• Decoding Mobile Client-
Page 44 and 45: Custom compression - the missing li
Page 46 and 47: Wrap Up - Rendering Performance•
Page 48 and 49: Future Work• GPU-friendly lossy c

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