We present Cube-4, a special-purpose volume rendering architecture that is capable of rendering high-resolution (e.g., 1024³) 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 interleaved memory, 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.

We present an efficient three-phase algorithm for volume viewing that is based on exploit- - t ing coherency between rays in parallel projection. The algorithm starts by building a ray emplate and determining a special plane for projection -- the base-plane. Parallel rays are cast t into the volume from within the projected region of the volume on the base-plane, by repeating he sequence of steps specified in the ray-template. We carefully choose the type of line to be s employed and the way the template is being placed on the base-plane in order to assure uniform ampling of the volume by the discrete rays. We conclude by describing an optimized software K implementation of our algorithm and reporting its performance. eywords: volume rendering, ray casting, template, parallel projection 1. Introduction Volume visualization is the process of converting complex volume data to a format that is p amenable to human understanding while maintaining the integrity and accuracy of the data. Th...

In computer graphics, rendering is the process by which an abstract description of a scene is converted to an image. When the scene is complex, or when high-quality images or high frame rates are required, the rendering process becomes computationally demanding. To provide the necessary levels of performance, parallel computing techniques must be brought to bear. Although parallelism has been exploited in computer graphics since the early days of the field, its initial use was primarily in specialized applications. The VLSI revolution of the late 1970Õs and the advent of scalable parallel computers during the late 1980Õs changed this situation. Today, parallel hardware is routinely used in graphics workstations, and numerous software-based rendering systems have been developed for general-purpose parallel architectures. This article provides a broad introduction to the subject of parallel rendering, encompassing both hardware and software systems. The focus is on the underlying concepts and the issues which arise in the design of parallel rendering algorithms and systems. We examine the different types of parallelism and how they can be applied in rendering applications. Concepts from parallel computing, such as data decomposition, task granularity, scalability, and load balancing, are considered in relation to the rendering

visualization, but also for modeling objects and structures derived from volumetric data. The paper describes work in progress towards demonstrating the utility of a voxel-based format for modeling physical interactions between virtual objects. Data structures are presented that help to optimize storage requirements and preserve object integrity during object movement. An adaptation to volume rendering algorithms is discussed that enables objects to be rendered individually and then combined in a final compositing step. Finally, algorithms and prototype systems are presented that use a voxel-based format to model physical interactions between objects. These physical interactions include collision detection and avoidance of object interpenetration, haptic, or tactile, exploration of virtual objects using a force feedback device, and object deformation.

This paper describes a high-performance special-purpose system, Cube-3, for displaying and manipulating high-resolution volumetric datasets in real-time. A primary goal of Cube-3 is to render 512³, 16-bit per voxel, datasets at about 30 frames per second. Cube-3 implements a ray-casting algorithm in a highly-parallel and pipelined architecture, using a 3D skewed volume memory, a modular fast bus, 2D skewed buffers, 3D interpolation and shading units, and a ray projection cone. Cube-3 will allow users to interactively visualize and investigate in real-time static (3D) and dynamic (4D) high-resolution volumetric datasets.

The two key issues in implementing a parallel ray-casting volume renderer are the work distribution and the data distribution. We have implemented such a renderer on the Fujitsu AP1000 using an adaptive image-space subdivision algorithm based on the worker-farm paradigm for the work distribution, and a distributed virtual memory, implemented in software, to provide the data distribution. Measurements show that this scheme works efficiently and effectively utilizes the data coherence that is inherent in volume data. Categories and Subject Descriptors: C.1.2 [Proces- sor Architectures]: Multiple Data Stream Architectures -- multiple-instruction-stream, multiple-data-stream (MIMD); I.3.1 [Computer Graphics]: Hardware Architecture -- parallel processing; I.3.7 [Computer Graphics]: ThreeDimensional Graphics and Realism -- ray tracing Key Words: Visualization, volume rendering, worker farm, image space, distributed virtual memory. 1 Introduction Volume rendering using ray-casting is a...

Real-time visualization of large volume datasets demands high performance computation, pushing the storage, processing, and data communication requirements to the limits of current technology. General purpose parallel processors have been used to visualize moderate size datasets at interactive frame rates; however, the cost and size of these supercomputers inhibits the widespread use for real-time visualization. This paper surveys several special purpose architectures that seek to render volumes at interactive rates. These specialized visualization accelerators have cost, performance, and size advantages over parallel processors. All architectures implement ray casting using parallel and pipelined hardware. We introduce a new metric that normalizes performance to compare these architectures. The architectures included in this survey are VOGUE, VIRIM, Array Based Ray Casting, EM-Cube, and VIZARD II. We also discuss future applications of special purpose accelerators.

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.

Many scientific and engineering disciplines, through physical measurements or computational simulations, generate large scale three-dimensional data sets. Both the physical size and the computational resources needed to render these data sets present a challenge to current rendering architectures and techniques. The Fujitsu AP1000 has the memory capacity and the processing speed to render large three-dimensional data sets at interactive or near-interactive speeds. A parallel version of a volume renderer has been implemented using a ray-casting technique on this architecture. The two key issues in implementing this technique on a distributed memory, MIMD machine such as the AP1000 are the work and data distribution. To perform the data distribution, a distributed virtual memory for volume data is used. The importance of utilizing the data coherence that is inherent in volume data is demonstrated through the analysis of several case studies. 1 Introduction Many scientific and engineerin...

We propose a new framework for doing scientific visualization. The basis for this framework is a combination of particle systems and behavioral animation. Here, particles are not only affected by the field that they are in, but can also exhibit different programmed behaviors. An intuitive delivery system, based on virtual cans of spray paint, is also described to introduce the smart particles into the data set. Hence the name spray rendering. Using this metaphor, different types of spray paint are used to highlight different features in the data set. Spray rendering offers several advantages over existing methods: (1) it generalizes the current techniques of surface, volume and flow visualization under one coherent framework; (2) it works with regular and irregular grids as well as sparse and dense data sets; (3) it allows selective progressive refinement; (4) it is modular, extensible and provides scientists with the flexibility for exploring relationships in their data sets in natura...