From Big Data to Big Displays: High-Performance Visualization at Blue Brain

Blue Brain has pushed high-performance visualization (HPV) to complement its HPC strategy since its inception in 2007. In 2011, this strategy has been accelerated to develop innovative visualization solutions through increased funding and strategic partnerships with other research institutions. We present the key elements of this HPV ecosystem, which integrates C++ visualization applications with novel collaborative display systems. We motivate how our strategy of transforming visualization engines into services enables a variety of use cases, not only for the integration with high-fidelity displays, but also to build service oriented architectures, to link into web applications and to provide remote services to Python applications.Comment: ISC 2017 Visualization at Scale worksho

Visual and interactive exploration of point data

Point data, such as Unit Postcodes (UPC), can provide very detailed information at fine scales of resolution. For instance, socio-economic attributes are commonly assigned to UPC. Hence, they can be represented as points and observable at the postcode level. Using UPC as a common field allows the concatenation of variables from disparate data sources that can potentially support sophisticated spatial analysis. However, visualising UPC in urban areas has at least three limitations. First, at small scales UPC occurrences can be very dense making their visualisation as points difficult. On the other hand, patterns in the associated attribute values are often hardly recognisable at large scales. Secondly, UPC can be used as a common field to allow the concatenation of highly multivariate data sets with an associated postcode. Finally, socio-economic variables assigned to UPC (such as the ones used here) can be non-Normal in their distributions as a result of a large presence of zero values and high variances which constrain their analysis using traditional statistics. This paper discusses a Point Visualisation Tool (PVT), a proof-of-concept system developed to visually explore point data. Various well-known visualisation techniques were implemented to enable their interactive and dynamic interrogation. PVT provides multiple representations of point data to facilitate the understanding of the relations between attributes or variables as well as their spatial characteristics. Brushing between alternative views is used to link several representations of a single attribute, as well as to simultaneously explore more than one variable. PVT’s functionality shows how the use of visual techniques embedded in an interactive environment enable the exploration of large amounts of multivariate point data

Interactive Visualization of the Largest Radioastronomy Cubes

3D visualization is an important data analysis and knowledge discovery tool, however, interactive visualization of large 3D astronomical datasets poses a challenge for many existing data visualization packages. We present a solution to interactively visualize larger-than-memory 3D astronomical data cubes by utilizing a heterogeneous cluster of CPUs and GPUs. The system partitions the data volume into smaller sub-volumes that are distributed over the rendering workstations. A GPU-based ray casting volume rendering is performed to generate images for each sub-volume, which are composited to generate the whole volume output, and returned to the user. Datasets including the HI Parkes All Sky Survey (HIPASS - 12 GB) southern sky and the Galactic All Sky Survey (GASS - 26 GB) data cubes were used to demonstrate our framework's performance. The framework can render the GASS data cube with a maximum render time < 0.3 second with 1024 x 1024 pixels output resolution using 3 rendering workstations and 8 GPUs. Our framework will scale to visualize larger datasets, even of Terabyte order, if proper hardware infrastructure is available.Comment: 15 pages, 12 figures, Accepted New Astronomy July 201

A review of data visualization: opportunities in manufacturing sequence management.

Data visualization now benefits from developments in technologies that offer innovative ways of presenting complex data. Potentially these have widespread application in communicating the complex information domains typical of manufacturing sequence management environments for global enterprises. In this paper the authors review the visualization functionalities, techniques and applications reported in literature, map these to manufacturing sequence information presentation requirements and identify the opportunities available and likely development paths. Current leading-edge practice in dynamic updating and communication with suppliers is not being exploited in manufacturing sequence management; it could provide significant benefits to manufacturing business. In the context of global manufacturing operations and broad-based user communities with differing needs served by common data sets, tool functionality is generally ahead of user application

SlicerAstro: a 3-D interactive visual analytics tool for HI data

SKA precursors are capable of detecting hundreds of galaxies in HI in a single 12 hours pointing. In deeper surveys one will probe more easily faint HI structures, typically located in the vicinity of galaxies, such as tails, filaments, and extraplanar gas. The importance of interactive visualization has proven to be fundamental for the exploration of such data as it helps users to receive immediate feedback when manipulating the data. We have developed SlicerAstro, a 3-D interactive viewer with new analysis capabilities, based on traditional 2-D input/output hardware. These capabilities enhance the data inspection, allowing faster analysis of complex sources than with traditional tools. SlicerAstro is an open-source extension of 3DSlicer, a multi-platform open source software package for visualization and medical image processing. We demonstrate the capabilities of the current stable binary release of SlicerAstro, which offers the following features: i) handling of FITS files and astronomical coordinate systems; ii) coupled 2-D/3-D visualization; iii) interactive filtering; iv) interactive 3-D masking; v) and interactive 3-D modeling. In addition, SlicerAstro has been designed with a strong, stable and modular C++ core, and its classes are also accessible via Python scripting, allowing great flexibility for user-customized visualization and analysis tasks.Comment: 18 pages, 11 figures, Accepted by Astronomy and Computing. SlicerAstro link: https://github.com/Punzo/SlicerAstro/wiki#get-slicerastr

Shape: A 3D Modeling Tool for Astrophysics

We present a flexible interactive 3D morpho-kinematical modeling application for astrophysics. Compared to other systems, our application reduces the restrictions on the physical assumptions, data type and amount that is required for a reconstruction of an object's morphology. It is one of the first publicly available tools to apply interactive graphics to astrophysical modeling. The tool allows astrophysicists to provide a-priori knowledge about the object by interactively defining 3D structural elements. By direct comparison of model prediction with observational data, model parameters can then be automatically optimized to fit the observation. The tool has already been successfully used in a number of astrophysical research projects.Comment: 13 pages, 11 figures, accepted for publication in the "IEEE Transactions on Visualization and Computer Graphics

Exploring Uncertainty in Geodemographics with Interactive Graphics

Geodemographic classifiers characterise populations by categorising geographical areas according to the demographic and lifestyle characteristics of those who live within them. The dimension-reducing quality of such classifiers provides a simple and effective means of characterising population through a manageable set of categories, but inevitably hides heterogeneity, which varies within and between the demographic categories and geographical areas, sometimes systematically. This may have implications for their use, which is widespread in government and commerce for planning, marketing and related activities. We use novel interactive graphics to delve into OAC – a free and open geodemographic classifier that classifies the UK population in over 200,000 small geographical areas into 7 super-groups, 21 groups and 52 sub-groups. Our graphics provide access to the original 41 demographic variables used in the classification and the uncertainty associated with the classification of each geographical area on-demand. It also supports comparison geographically and by category. This serves the dual purpose of helping understand the classifier itself leading to its more informed use and providing a more comprehensive view of population in a comprehensible manner. We assess the impact of these interactive graphics on experienced OAC users who explored the details of the classification, its uncertainty and the nature of between – and within – class variation and then reflect on their experiences. Visualization of the complexities and subtleties of the classification proved to be a thought-provoking exercise both confirming and challenging users’ understanding of population, the OAC classifier and the way it is used in their organisations. Users identified three contexts for which the techniques were deemed useful in the context of local government, confirming the validity of the proposed methods