60 research outputs found
Visualization techniques to aid in the analysis of multi-spectral astrophysical data sets
This report describes our project activities for the period Sep. 1991 - Oct. 1992. Our activities included stabilizing the software system STAR, porting STAR to IDL/widgets (improved user interface), targeting new visualization techniques for multi-dimensional data visualization (emphasizing 3D visualization), and exploring leading-edge 3D interface devices. During the past project year we emphasized high-end visualization techniques, by exploring new tools offered by state-of-the-art visualization software (such as AVS3 and IDL4/widgets), by experimenting with tools still under research at the Department of Computer Science (e.g., use of glyphs for multidimensional data visualization), and by researching current 3D input/output devices as they could be used to explore 3D astrophysical data. As always, any project activity is driven by the need to interpret astrophysical data more effectively
IRAS software analysis library
The goal of this project was to collect 'research software' written in Interactive Data Language (IDL) to support analysis of data from the Infrared Astronomical Satellite (IRAS) and make it available to the larger community. 'Research Software' describes software created by researchers and staff for a specific research goal, but lacks sufficient documentation, easy to use interfaces, and rigorous debugging. Additionally, most of the IDL/IRAS code available needed to be ported to a (largely) hardware independent new version of IDL
Multiple incidence angle SIR-B experiment over Argentina
The Shuttle Imaging Radar (SIR-B), the second synthetic aperture radar (SAR) to fly aboard a shuttle, was launched on October 5, 1984. One of the primary goals of the SIR-B experiment was to use multiple incidence angle radar images to distinguish different terrain types through the use of their characteristic backscatter curves. This goal was accomplished in several locations including the Chubut Province of southern Argentina. Four descending image acquisitions were collected providing a multiple incidence angle image set. The data were first used to assess stereo-radargrammetric techniques. A digital elevation model was produced using the optimum pair of multiple incidence angle images. This model was then used to determine the local incidence angle of each picture element to generate curves of relative brightness vs. incidence angle. Secondary image products were also generated using the multi-angle data. The results of this work indicate that: (1) various forest species and various structures of a single species may be discriminated using multiple incidence angle radar imagery, and (2) it is essential to consider the variation in backscatter due to a variable incidence angle when analyzing and comparing data collected at varying frequencies and polarizations
Visualization techniques to aid in the analysis of multispectral astrophysical data sets
The goal of this project was to support the scientific analysis of multi-spectral astrophysical data by means of scientific visualization. Scientific visualization offers its greatest value if it is not used as a method separate or alternative to other data analysis methods but rather in addition to these methods. Together with quantitative analysis of data, such as offered by statistical analysis, image or signal processing, visualization attempts to explore all information inherent in astrophysical data in the most effective way. Data visualization is one aspect of data analysis. Our taxonomy as developed in Section 2 includes identification and access to existing information, preprocessing and quantitative analysis of data, visual representation and the user interface as major components to the software environment of astrophysical data analysis. In pursuing our goal to provide methods and tools for scientific visualization of multi-spectral astrophysical data, we therefore looked at scientific data analysis as one whole process, adding visualization tools to an already existing environment and integrating the various components that define a scientific data analysis environment. As long as the software development process of each component is separate from all other components, users of data analysis software are constantly interrupted in their scientific work in order to convert from one data format to another, or to move from one storage medium to another, or to switch from one user interface to another. We also took an in-depth look at scientific visualization and its underlying concepts, current visualization systems, their contributions and their shortcomings. The role of data visualization is to stimulate mental processes different from quantitative data analysis, such as the perception of spatial relationships or the discovery of patterns or anomalies while browsing through large data sets. Visualization often leads to an intuitive understanding of the meaning of data values and their relationships by sacrificing accuracy in interpreting the data values. In order to be accurate in the interpretation, data values need to be measured, computed on, and compared to theoretical or empirical models (quantitative analysis). If visualization software hampers quantitative analysis (which happens with some commercial visualization products), its use is greatly diminished for astrophysical data analysis. The software system STAR (Scientific Toolkit for Astrophysical Research) was developed as a prototype during the course of the project to better understand the pragmatic concerns raised in the project. STAR led to a better understanding on the importance of collaboration between astrophysicists and computer scientists. Twenty-one examples of the use of visualization for astrophysical data are included with this report. Sixteen publications related to efforts performed during or initiated through work on this project are listed at the end of this report
Visualization techniques to aid in the analysis of multi-spectral astrophysical data sets
The goal of this project was to support the scientific analysis of multi-spectral astrophysical data by means of scientific visualization. Scientific visualization offers its greatest value if it is not used as a method separate or alternative to other data analysis methods but rather in addition to these methods. Together with quantitative analysis of data, such as offered by statistical analysis, image or signal processing, visualization attempts to explore all information inherent in astrophysical data in the most effective way. Data visualization is one aspect of data analysis. Our taxonomy as developed in Section 2 includes identification and access to existing information, preprocessing and quantitative analysis of data, visual representation and the user interface as major components to the software environment of astrophysical data analysis. In pursuing our goal to provide methods and tools for scientific visualization of multi-spectral astrophysical data, we therefore looked at scientific data analysis as one whole process, adding visualization tools to an already existing environment and integrating the various components that define a scientific data analysis environment. As long as the software development process of each component is separate from all other components, users of data analysis software are constantly interrupted in their scientific work in order to convert from one data format to another, or to move from one storage medium to another, or to switch from one user interface to another. We also took an in-depth look at scientific visualization and its underlying concepts, current visualization systems, their contributions, and their shortcomings. The role of data visualization is to stimulate mental processes different from quantitative data analysis, such as the perception of spatial relationships or the discovery of patterns or anomalies while browsing through large data sets. Visualization often leads to an intuitive understanding of the meaning of data values and their relationships by sacrificing accuracy in interpreting the data values. In order to be accurate in the interpretation, data values need to be measured, computed on, and compared to theoretical or empirical models (quantitative analysis). If visualization software hampers quantitative analysis (which happens with some commercial visualization products), its use is greatly diminished for astrophysical data analysis. The software system STAR (Scientific Toolkit for Astrophysical Research) was developed as a prototype during the course of the project to better understand the pragmatic concerns raised in the project. STAR led to a better understanding on the importance of collaboration between astrophysicists and computer scientists
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Foreword to the special section on computer graphics and related subjects education in the time of Covid
PHIST: a Pipelined, Hybrid-parallel Iterative Solver Toolkit
The increasing complexity of hardware and software environments in high-performance computing poses big challenges on the
development of sustainable and hardware-efcient numerical software. This paper addresses these challenges in the context of sparse
solvers. Existing solutions typically target sustainability, flexibility or performance, but rarely all of them.
Our new library PHIST provides implementations of solvers for sparse linear systems and eigenvalue problems. It is a productivity
platform for performance-aware developers of algorithms and application software with abstractions that do not obscure the view on
hardware-software interaction.
The PHIST software architecture and the PHIST development process were designed to overcome shortcomings of existing packages.
An interface layer for basic sparse linear algebra functionality that can be provided by multiple backends ensures sustainability, and
PHIST supports common techniques for improving scalability and performance of algorithms such as blocking and kernel fusion.
We showcase these concepts using the PHIST implementation of a block Jacobi-Davidson solver for non-Hermitian and generalized
eigenproblems. We study its performance on a multi-core CPU, a GPU and a large-scale many-core system. Furthermore, we show
how an existing implementation of a block Krylov-Schur method in the Trilinos package Anasazi can beneft from the performance
engineering techniques used in PHIST
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