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

Animated computer graphics models of space and earth sciences data generated via the massively parallel processor

The capability was developed of rapidly producing visual representations of large, complex, multi-dimensional space and earth sciences data sets via the implementation of computer graphics modeling techniques on the Massively Parallel Processor (MPP) by employing techniques recently developed for typically non-scientific applications. Such capabilities can provide a new and valuable tool for the understanding of complex scientific data, and a new application of parallel computing via the MPP. A prototype system with such capabilities was developed and integrated into the National Space Science Data Center's (NSSDC) Pilot Climate Data System (PCDS) data-independent environment for computer graphics data display to provide easy access to users. While developing these capabilities, several problems had to be solved independently of the actual use of the MPP, all of which are outlined

A sparse octree gravitational N-body code that runs entirely on the GPU processor

We present parallel algorithms for constructing and traversing sparse octrees on graphics processing units (GPUs). The algorithms are based on parallel-scan and sort methods. To test the performance and feasibility, we implemented them in CUDA in the form of a gravitational tree-code which completely runs on the GPU.(The code is publicly available at: http://castle.strw.leidenuniv.nl/software.html) The tree construction and traverse algorithms are portable to many-core devices which have support for CUDA or OpenCL programming languages. The gravitational tree-code outperforms tuned CPU code during the tree-construction and shows a performance improvement of more than a factor 20 overall, resulting in a processing rate of more than 2.8 million particles per second.Comment: Accepted version. Published in Journal of Computational Physics. 35 pages, 12 figures, single colum

Cross Teaching Parallelism and Ray Tracing: A Project-based Approach to Teaching Applied Parallel Computing

Massively parallel Graphics Processing Unit (GPU) hardware has become increasingly powerful, available and affordable. Software tools have also advanced to the point that programmers can write general purpose parallel programs that take advantage of the large number of compute cores available in the hardware. With literally hundreds of compute cores available on a single device, program performance can increase by orders of magnitude. We believe that introducing students to the concepts of parallel programming for massively parallel hardware is of increasing importance in an undergraduate computer science curriculum. Furthermore, we believe that students learn best when given projects that reflect real problems in computer science. This paper describes the experience of integrating two undergraduate computer science courses to enhance student learning in parallel computing concepts. In this cross teaching experience we structured the integration of the courses such that students studying parallel computing worked with students studying advanced rendering for approximately 30% of the quarter long courses. Working in teams on a joint project, both groups of students were able to see the application of parallelization to an existing software project with both the benefits and complications exposed early in the curriculum of both courses. Motivating projects and performance gains are discussed, as well as student survey data on the effectiveness of the learning outcomes. Both performance and survey data indicate a positive gain from the cross teaching experience

GPU-Based Volume Rendering of Noisy Multi-Spectral Astronomical Data

Traditional analysis techniques may not be sufficient for astronomers to make the best use of the data sets that current and future instruments, such as the Square Kilometre Array and its Pathfinders, will produce. By utilizing the incredible pattern-recognition ability of the human mind, scientific visualization provides an excellent opportunity for astronomers to gain valuable new insight and understanding of their data, particularly when used interactively in 3D. The goal of our work is to establish the feasibility of a real-time 3D monitoring system for data going into the Australian SKA Pathfinder archive. Based on CUDA, an increasingly popular development tool, our work utilizes the massively parallel architecture of modern graphics processing units (GPUs) to provide astronomers with an interactive 3D volume rendering for multi-spectral data sets. Unlike other approaches, we are targeting real time interactive visualization of datasets larger than GPU memory while giving special attention to data with low signal to noise ratio - two critical aspects for astronomy that are missing from most existing scientific visualization software packages. Our framework enables the astronomer to interact with the geometrical representation of the data, and to control the volume rendering process to generate a better representation of their datasets.Comment: 4 pages, 1 figure, to appear in the proceedings of ADASS XIX, Oct 4-8 2009, Sapporo, Japan (ASP Conf. Series

Sort computation

Sorting has long been used to organize data in preparation for further computation, but sort computation allows some types of computation to be performed during the sort. Sort aggregation and sort distribution are the two basic forms of sort computation. Sort aggregation generates an accumulative or aggregate result for each group of records and places this result in one of the records. An aggregate operation can be any operation that is both associative and commutative, i.e., any operation whose result does not depend on the order of the operands or the order in which the operations are performed. Sort distribution copies the value from a field of a specific record in a group into that field in every record of that group