3 research outputs found
A Distributed GPU-based Framework for real-time 3D Volume Rendering of Large Astronomical Data Cubes
We present a framework to interactively volume-render three-dimensional data
cubes using distributed ray-casting and volume bricking over a cluster of
workstations powered by one or more graphics processing units (GPUs) and a
multi-core CPU. The main design target for this framework is to provide an
in-core visualization solution able to provide three-dimensional interactive
views of terabyte-sized data cubes. We tested the presented framework using a
computing cluster comprising 64 nodes with a total of 128 GPUs. The framework
proved to be scalable to render a 204 GB data cube with an average of 30 frames
per second. Our performance analyses also compare between using NVIDIA Tesla
1060 and 2050 GPU architectures and the effect of increasing the visualization
output resolution on the rendering performance. Although our initial focus, and
the examples presented in this work, is volume rendering of spectral data cubes
from radio astronomy, we contend that our approach has applicability to other
disciplines where close to real-time volume rendering of terabyte-order 3D data
sets is a requirement.Comment: 13 Pages, 7 figures, has been accepted for publication in
Publications of the Astronomical Society of Australi
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