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

Massively Parallel Ray Tracing Algorithm Using GPU

Ray tracing is a technique for generating an image by tracing the path of light through pixels in an image plane and simulating the effects of high-quality global illumination at a heavy computational cost. Because of the high computation complexity, it can't reach the requirement of real-time rendering. The emergence of many-core architectures, makes it possible to reduce significantly the running time of ray tracing algorithm by employing the powerful ability of floating point computation. In this paper, a new GPU implementation and optimization of the ray tracing to accelerate the rendering process is presented

Construction and Evaluation of an Ultra Low Latency Frameless Renderer for VR.

© 2016 IEEE.Latency-the delay between a users action and the response to this action-is known to be detrimental to virtual reality. Latency is typically considered to be a discrete value characterising a delay, constant in time and space-but this characterisation is incomplete. Latency changes across the display during scan-out, and how it does so is dependent on the rendering approach used. In this study, we present an ultra-low latency real-time ray-casting renderer for virtual reality, implemented on an FPGA. Our renderer has a latency of 1 ms from tracker to pixel. Its frameless nature means that the region of the display with the lowest latency immediately follows the scan-beam. This is in contrast to frame-based systems such as those using typical GPUs, for which the latency increases as scan-out proceeds. Using a series of high and low speed videos of our system in use, we confirm its latency of 1 ms. We examine how the renderer performs when driving a traditional sequential scan-out display on a readily available HMO, the Oculus Rift OK2. We contrast this with an equivalent apparatus built using a GPU. Using captured human head motion and a set of image quality measures, we assess the ability of these systems to faithfully recreate the stimuli of an ideal virtual reality system-one with a zero latency tracker, renderer and display running at 1 kHz. Finally, we examine the results of these quality measures, and how each rendering approach is affected by velocity of movement and display persistence. We find that our system, with a lower average latency, can more faithfully draw what the ideal virtual reality system would. Further, we find that with low display persistence, the sensitivity to velocity of both systems is lowered, but that it is much lower for ours

Adaptive transfer functions: improved multiresolution visualization of medical models

The final publication is available at Springer via http://dx.doi.org/10.1007/s00371-016-1253-9Medical datasets are continuously increasing in size. Although larger models may be available for certain research purposes, in the common clinical practice the models are usually of up to 512x512x2000 voxels. These resolutions exceed the capabilities of conventional GPUs, the ones usually found in the medical doctors’ desktop PCs. Commercial solutions typically reduce the data by downsampling the dataset iteratively until it fits the available target specifications. The data loss reduces the visualization quality and this is not commonly compensated with other actions that might alleviate its effects. In this paper, we propose adaptive transfer functions, an algorithm that improves the transfer function in downsampled multiresolution models so that the quality of renderings is highly improved. The technique is simple and lightweight, and it is suitable, not only to visualize huge models that would not fit in a GPU, but also to render not-so-large models in mobile GPUs, which are less capable than their desktop counterparts. Moreover, it can also be used to accelerate rendering frame rates using lower levels of the multiresolution hierarchy while still maintaining high-quality results in a focus and context approach. We also show an evaluation of these results based on perceptual metrics.Peer ReviewedPostprint (author's final draft