Cube-4 - A Scalable Architecture for Real-Time Volume Rendering

We present Cube-4, a special-purpose volume rendering architecture that is capable of rendering high-resolution (e.g., 1024^3) datasets at 30 frames per second. The underlying algorithm, called slice-parallel ray-casting, uses tri-linear interpolation of samples between data slices for parallel and perspective projections. The architecture uses a distributed interleavedmemory, several parallel processing pipelines, and an innovative parallel dataflow scheme that requires no global communication, except at the pixel level. This leads to local, fixed bandwidth interconnections and has the benefits of high memory bandwidth, real-time data input, modularity, and scalability. We have simulated the architecture and have implemented a working prototype of the complete hardware on a configurable custom hardware machine. Our results indicate true real-time performance for high-resolution datasets and linear scalability of performance with the number of processing pipelines.Engineering and Applied Science

Towards a Scalable Architecture for Real-Time Volume Rendering

In this paper we present our research eff orts towards a scalable volume rendering architecture for the real-time visualization of dynamically changing high-resolution datasets. Using a linearly skewed memory interleaving we were able to develop a parallel data ow model that leads to local, fixed-bandwidth interconnections between processing elements. This parallel dataflow model diff ers from previous work in that it requires no global communication of data except at the pixel level. Using this data ow model we are developing Cube-4, an architecture that is scalable to very high performances and allows for modular and extensible hardware implementations.Engineering and Applied Science

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

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

EM-Cube: An Architecture for Low-Cost Real-Time Volume Rendering

EM-Cube is a VLSI architecture for low-cost, high quality volume rendering at full video frame rates. Derived from the Cube-4 architecture developed at SUNY at Stony Brook, EM-Cube computes sample points and gradients on-the-fly to project 3-dimensional volume data onto 2-dimensional images with realistic lighting and shading. A modest rendering system based on EM-Cube consists of a PCI card with four rendering chips (ASICs), four 64Mbit SDRAMs to hold the volume data, and four SRAMs to capture the rendered image. The performance target for this configuration is to render images from a 256^3 x 16 bit data set at 30 frames/sec. The EM-Cube architecture can be scaled to larger volume data-sets and/or higher frame rates by adding additional ASICs, SDRAMs, and SRAMs. This paper addresses three major challenges encountered developing EM-Cube into a practical product: exploiting the bandwidth inherent in the SDRAMs containing the volume data, keeping the pin-count between adjacent ASICs at a tractable level, and reducing the on-chip storage required to hold the intermediate results of rendering.Engineering and Applied Science

A Distributed GPU-based Framework for real-time 3D Volume Rendering of Large Astronomical Data Cubes

We present a framework to interactively volume-render three-dimensional data cubes using distributed ray-casting and volume bricking over a cluster of workstations powered by one or more graphics processing units (GPUs) and a multi-core CPU. The main design target for this framework is to provide an in-core visualization solution able to provide three-dimensional interactive views of terabyte-sized data cubes. We tested the presented framework using a computing cluster comprising 64 nodes with a total of 128 GPUs. The framework proved to be scalable to render a 204 GB data cube with an average of 30 frames per second. Our performance analyses also compare between using NVIDIA Tesla 1060 and 2050 GPU architectures and the effect of increasing the visualization output resolution on the rendering performance. Although our initial focus, and the examples presented in this work, is volume rendering of spectral data cubes from radio astronomy, we contend that our approach has applicability to other disciplines where close to real-time volume rendering of terabyte-order 3D data sets is a requirement.Comment: 13 Pages, 7 figures, has been accepted for publication in Publications of the Astronomical Society of Australi

Three architectures for volume rendering

Volume rendering is a key technique in scientific visualization that lends itself to significant exploitable parallelism. The high computational demands of real-time volume rendering and continued technological advances in the area of VLSI give impetus to the development of special-purpose volume rendering architectures. This paper presents and characterizes three recently developed volume rendering engines which are based on the ray-casting method. A taxonomy of the algorithmic variants of ray-casting and details of each ray-casting architecture are discussed. The paper then compares the machine features and provides an outlook on future developments in the area of volume rendering hardware

Distributed OpenGL Rendering in Network Bandwidth Constrained Environments

Display walls made from multiple monitors are often used when very high resolution images are required. To utilise a display wall, rendering information must be sent to each computer that the monitors are connect to. The network is often the performance bottleneck for demanding applications, like high performance 3D animations. This paper introduces ClusterGL; a distribution library for OpenGL applications. ClusterGL reduces network traffic by using compression, frame differencing and multi-cast. Existing applications can use ClusterGL without recompilation. Benchmarks show that, for most applications, ClusterGL outperforms other systems that support unmodified OpenGL applications including Chromium and BroadcastGL. The difference is larger for more complex scene geometries and when there are more display machines. For example, when rendering OpenArena, ClusterGL outperforms Chromium by over 300% on the Symphony display wall at The University of Waikato, New Zealand. This display has 20 monitors supported by five computers connected by gigabit Ethernet, with a full resolution of over 35 megapixels. ClusterGL is freely available via Google Code