43 research outputs found
Optimizing Lossy Compression Rate-Distortion from Automatic Online Selection between SZ and ZFP
With ever-increasing volumes of scientific data produced by HPC applications,
significantly reducing data size is critical because of limited capacity of
storage space and potential bottlenecks on I/O or networks in writing/reading
or transferring data. SZ and ZFP are the two leading lossy compressors
available to compress scientific data sets. However, their performance is not
consistent across different data sets and across different fields of some data
sets: for some fields SZ provides better compression performance, while other
fields are better compressed with ZFP. This situation raises the need for an
automatic online (during compression) selection between SZ and ZFP, with a
minimal overhead. In this paper, the automatic selection optimizes the
rate-distortion, an important statistical quality metric based on the
signal-to-noise ratio. To optimize for rate-distortion, we investigate the
principles of SZ and ZFP. We then propose an efficient online, low-overhead
selection algorithm that predicts the compression quality accurately for two
compressors in early processing stages and selects the best-fit compressor for
each data field. We implement the selection algorithm into an open-source
library, and we evaluate the effectiveness of our proposed solution against
plain SZ and ZFP in a parallel environment with 1,024 cores. Evaluation results
on three data sets representing about 100 fields show that our selection
algorithm improves the compression ratio up to 70% with the same level of data
distortion because of very accurate selection (around 99%) of the best-fit
compressor, with little overhead (less than 7% in the experiments).Comment: 14 pages, 9 figures, first revisio
Web-Based Visualization of Very Large Scientific Astronomy Imagery
Visualizing and navigating through large astronomy images from a remote
location with current astronomy display tools can be a frustrating experience
in terms of speed and ergonomics, especially on mobile devices. In this paper,
we present a high performance, versatile and robust client-server system for
remote visualization and analysis of extremely large scientific images.
Applications of this work include survey image quality control, interactive
data query and exploration, citizen science, as well as public outreach. The
proposed software is entirely open source and is designed to be generic and
applicable to a variety of datasets. It provides access to floating point data
at terabyte scales, with the ability to precisely adjust image settings in
real-time. The proposed clients are light-weight, platform-independent web
applications built on standard HTML5 web technologies and compatible with both
touch and mouse-based devices. We put the system to the test and assess the
performance of the system and show that a single server can comfortably handle
more than a hundred simultaneous users accessing full precision 32 bit
astronomy data.Comment: Published in Astronomy & Computing. IIPImage server available from
http://iipimage.sourceforge.net . Visiomatic code and demos available from
http://www.visiomatic.org
Is Smaller Always Better? - Evaluating Video Compression Techniques for Simulation Ensembles
We provide an evaluation of the applicability of video compression techniques for compressing visualization image databases that are often used for in situ visualization. Considering relevant practical implementation aspects, we identify relevant compression parameters, and evaluate video compression for several test cases, involving several data sets and visualization methods; we use three different video codecs. To quantify the benefits and drawbacks of video compression, we employ metrics for image quality, compression rate, and performance. The experiments discussed provide insight into good choices of parameter values, working well in the considered cases
Symmetric and Asymmetric Data in Solution Models
This book is a Printed Edition of the Special Issue that covers research on symmetric and asymmetric data that occur in real-life problems. We invited authors to submit their theoretical or experimental research to present engineering and economic problem solution models that deal with symmetry or asymmetry of different data types. The Special Issue gained interest in the research community and received many submissions. After rigorous scientific evaluation by editors and reviewers, seventeen papers were accepted and published. The authors proposed different solution models, mainly covering uncertain data in multicriteria decision-making (MCDM) problems as complex tools to balance the symmetry between goals, risks, and constraints to cope with the complicated problems in engineering or management. Therefore, we invite researchers interested in the topics to read the papers provided in the book
Evaluation and implementation of an auto-encoder for compression of satellite images in the ScOSA project
The thesis evaluates the efficiency of various autoencoder neural networks for image compression regarding satellite imagery. The results highlight the evaluation and implementation of autoencoder architectures and the procedures required to deploy neural networks to reliable embedded devices. The developed autoencoders evaluated, targeting a ZYNQ 7020 FPGA (Field Programmable Gate Array) and a ZU7EV FPGA
Evaluation and implementation of an auto-encoder for compression of satellite images in the ScOSA project
The thesis evaluates the efficiency of various autoencoder neural networks for image compression regarding satellite imagery. The results highlight the evaluation and implementation of autoencoder architectures and the procedures required to deploy neural networks to reliable embedded devices. The developed autoencoders evaluated, targeting a ZYNQ 7020 FPGA (Field Programmable Gate Array) and a ZU7EV FPGA
Streaming Low-Rank Matrix Approximation with an Application to Scientific Simulation
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of data matrices that arise from large-scale scientific simulations and data collection. The technical contribution consists in a new algorithm for constructing an accurate low-rank approximation of a matrix from streaming data. This method is accompanied by an a priori analysis that allows the user to set algorithm parameters with confidence and an a posteriori error estimator that allows the user to validate the quality of the reconstructed matrix. In comparison to previous techniques, the new method achieves smaller relative approximation errors and is less sensitive to parameter choices. As concrete applications, the paper outlines how the algorithm can be used to compress a Navier--Stokes simulation and a sea surface temperature dataset
Technology, Science and Culture
From the success of the first and second volume of this series, we are enthusiastic to continue our discussions on research topics related to the fields of Food Science, Intelligent Systems, Molecular Biomedicine, Water Science, and Creation and Theories of Culture. Our aims are to discuss the newest topics, theories, and research methods in each of the mentioned fields, to promote debates among top researchers and graduate students and to generate collaborative works among them