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

Developing a raster detector system with the J array processing language

All digital copying aims to reproduce an original image as faithfully as possible under certain constraints. In the past, image processing had to be implemented in hardware for performance reasons. Here, a 100% software solution is outlined. In order to find such a solution an appropriate methodology based on the array processing language J is used. Although J is ideal for prototyping such designs, its wider application is seriously hindered by the lack of awareness of array processing languages amongst engineers, and by the lack of available education in this language and methodology

A Scalable Tile Map Service for Distributing Dynamic Choropleth Maps

In this paper we propose a solution to several key limitations of current web based mapping systems: slow rendering speeds and the restriction of online map viewing to a small number of areal units as well as a limited number of users. Our approach is implemented as a Scalable Tile Map Service that distributes dynamic choropleth maps in real-time through a new caching methodology. This new Map Service lays the foundation for advances in web based applications reliant on dynamic map rendering such as emergency management systems and interactive exploratory spatial data analysis. We present the results of an empirical illustration in which this new methodology is used to facilitate collaborative decision making by visualizing spatial outcomes of simulation results on the fly.

GIS in the cloud: implementing a web map service on Google App Engine

Many producers of geographic information are now disseminating their data using open web service protocols, notably those published by the Open Geospatial Consortium. There are many challenges inherent in running robust and reliable services at reasonable cost. Cloud computing provides a new kind of scalable infrastructure that could address many of these challenges. In this study we implement a Web Map Service for raster imagery within the Google App Engine environment. We discuss the challenges of developing GIS applications within this framework and the performance characteristics of the implementation. Results show that the application scales well to multiple simultaneous users and performance will be adequate for many applications, although concerns remain over issues such as latency spikes. We discuss the feasibility of implementing services within the free usage quotas of Google App Engine and the possibility of extending the approaches in this paper to other GIS applications

Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines

In this paper, we address the problem of efficient execution of a computation pattern, referred to here as the irregular wavefront propagation pattern (IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in several image processing operations. In the IWPP, data elements in the wavefront propagate waves to their neighboring elements on a grid if a propagation condition is satisfied. Elements receiving the propagated waves become part of the wavefront. This pattern results in irregular data accesses and computations. We develop and evaluate strategies for efficient computation and propagation of wavefronts using a multi-level queue structure. This queue structure improves the utilization of fast memories in a GPU and reduces synchronization overheads. We also develop a tile-based parallelization strategy to support execution on multiple CPUs and GPUs. We evaluate our approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs and 2 multicore CPUs) using the IWPP implementations of two widely used image processing operations: morphological reconstruction and euclidean distance transform. Our results show significant performance improvements on GPUs. The use of multiple CPUs and GPUs cooperatively attains speedups of 50x and 85x with respect to single core CPU executions for morphological reconstruction and euclidean distance transform, respectively.Comment: 37 pages, 16 figure