@inproceedings{
 title = {In situ video encoding of floating-point volume data using special-purpose hardware for a posteriori rendering and analysis},
 type = {inproceedings},
 year = {2017},
 keywords = {Floating-point compression,GPU video encoding,Volume compression},
 pages = {64-73},
 volume = {2017-Decem},
 websites = {http://ieeexplore.ieee.org/document/8231852/},
 month = {4},
 publisher = {IEEE},
 id = {ae2fd0e8-eb3b-34bb-ae02-c84319d53fb9},
 created = {2021-04-09T15:23:18.258Z},
 file_attached = {false},
 profile_id = {75799766-8e2d-3c98-81f9-e3efa41233d0},
 group_id = {c9329632-2a50-3043-b803-cadc8dbdfc3f},
 last_modified = {2021-04-09T15:23:18.258Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {Scientific simulations typically store only a small fraction of computed timesteps due to storage and I/O bandwidth limitations. Previous work has demonstrated the compressibility of floating-point volume data, but such compression often comes with a tradeoff between computational complexity and the achievable compression ratio. This work demonstrates the use of special-purpose video encoding hardware on the GPU which is present but (to the best of our knowledge) completely unused in current GPU-equipped super computers such as Titan. We show that lossy encoding allows the output of far more data at sufficient quality for a posteriori rendering and analysis. We also show that the encoding can be computed in parallel to general-purpose computation due to the special-purpose hardware. Finally, we demonstrate such encoded volumes are inexpensive to decode in memory during analysis, making it unnecessary to ever store the decompressed volumes on disk.},
 bibtype = {inproceedings},
 author = {Leaf, Nick and Miller, Bob and Ma, Kwan Liu},
 doi = {10.1109/LDAV.2017.8231852},
 booktitle = {2017 IEEE 7th Symposium on Large Data Analysis and Visualization, LDAV 2017}
}

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