1,640 research outputs found
GPU acceleration of predictive partitioned vector quantization for ultraspectral sounder data compression
[[abstract]]For the large-volume ultraspectral sounder data, compression is desirable to save storage space and transmission time. To retrieve the geophysical paramters without losing precision the ultraspectral sounder data compression has to be lossless. Recently there is a boom on the use of graphic processor units (GPU) for speedup of scientific computations. By identifying the time dominant portions of the code that can be executed in parallel, significant speedup can be achieved by using GPU. Predictive partitioned vector quantization (PPVQ) has been proven to be an effective lossless compression scheme for ultraspectral sounder data. It consists of linear prediction, bit depth partitioning, vector quantization, and entropy coding. Two most time consuming stages of linear prediction and vector quantization are chosen for GPU-based implementation. By exploiting the data parallel characteristics of these two stages, a spatial division design shows a speedup of 72x in our four-GPU-based implementation of the PPVQ compression scheme.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[booktype]]紙本[[countrycodes]]US
Fast Compressed Segmentation Volumes for Scientific Visualization
Voxel-based segmentation volumes often store a large number of labels and
voxels, and the resulting amount of data can make storage, transfer, and
interactive visualization difficult. We present a lossless compression
technique which addresses these challenges. It processes individual small
bricks of a segmentation volume and compactly encodes the labelled regions and
their boundaries by an iterative refinement scheme. The result for each brick
is a list of labels, and a sequence of operations to reconstruct the brick
which is further compressed using rANS-entropy coding. As the relative
frequencies of operations are very similar across bricks, the entropy coding
can use global frequency tables for an entire data set which enables efficient
and effective parallel (de)compression. Our technique achieves high throughput
(up to gigabytes per second both for compression and decompression) and strong
compression ratios of about 1% to 3% of the original data set size while being
applicable to GPU-based rendering. We evaluate our method for various data sets
from different fields and demonstrate GPU-based volume visualization with
on-the-fly decompression, level-of-detail rendering (with optional on-demand
streaming of detail coefficients to the GPU), and a caching strategy for
decompressed bricks for further performance improvement.Comment: IEEE Vis 202
Data Compression in the Petascale Astronomy Era: a GERLUMPH case study
As the volume of data grows, astronomers are increasingly faced with choices
on what data to keep -- and what to throw away. Recent work evaluating the
JPEG2000 (ISO/IEC 15444) standards as a future data format standard in
astronomy has shown promising results on observational data. However, there is
still a need to evaluate its potential on other type of astronomical data, such
as from numerical simulations. GERLUMPH (the GPU-Enabled High Resolution
cosmological MicroLensing parameter survey) represents an example of a data
intensive project in theoretical astrophysics. In the next phase of processing,
the ~27 terabyte GERLUMPH dataset is set to grow by a factor of 100 -- well
beyond the current storage capabilities of the supercomputing facility on which
it resides. In order to minimise bandwidth usage, file transfer time, and
storage space, this work evaluates several data compression techniques.
Specifically, we investigate off-the-shelf and custom lossless compression
algorithms as well as the lossy JPEG2000 compression format. Results of
lossless compression algorithms on GERLUMPH data products show small
compression ratios (1.35:1 to 4.69:1 of input file size) varying with the
nature of the input data. Our results suggest that JPEG2000 could be suitable
for other numerical datasets stored as gridded data or volumetric data. When
approaching lossy data compression, one should keep in mind the intended
purposes of the data to be compressed, and evaluate the effect of the loss on
future analysis. In our case study, lossy compression and a high compression
ratio do not significantly compromise the intended use of the data for
constraining quasar source profiles from cosmological microlensing.Comment: 15 pages, 9 figures, 5 tables. Published in the Special Issue of
Astronomy & Computing on The future of astronomical data format
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