At its World-Wide Developers Conference Apple introduced Core Image as a feature of its upcoming Tiger release. Core Image is a framework for image processing on the GPU using a modified stream processing paradigm. Core Image is an interesting computational framework for offloading some general-purpose computations on to the GPU. It appears to be the first commercial effort to offer a general image computing environment for GPUs. The library comes with 100 basic plugins, called "Image Units", and can be extended by developers. The computing model is based on stream processing, where each kernel is expressed in a high-level language and computes a result image based on some number of input images. The kernels can be strung together in arbitrary image computation "graphs", in a model similar to that described by Michael Shantzis in his 1994 paper A Model for Efficient and Flexible Image Computing. Registered Apple Developers (free registration) can access a pre-release version of Core Image.

Jahshaka is an open-source, real-time editing, effects and image processing application that works in 3D space. The 1.9a8 release of jahshaka, available today, is supports GPU-accelerated image processing. The Jahshaka developers' research in real-time image processing using the GPU is described in a white paper.

This paper presents an extensible system for interactively rendering multiple types of ray-casted objects in a manner compatible with pre-existing rendering engines. The sample implementation includes support for general quadrics and volumetric isosurfaces. It also includes a high-speed sphere renderer, and of course a standard triangle-rendering pipeline. The system is designed so that most of the algorithms designed to run on the existing raster engine can be added with minimal overhead/coding effort. We have demonstrated shadowing using the shadow-map algorithm. ("Beyond Triangles: A Simple Framework For Hardware-Accelerated Non-Triangular Primitives", To be Submitted for publication.)

These works from the Database Systems Lab at UC Santa Barbara describe how a graphics processor can be effectively used to accelerate the performance of spatial database (GIS databases) operations. Spatial database operations, especially which involve polygon datasets, have been known to be computationally expensive. Sun et al. describe a novel hardware / software co-processing technique which uses basic features of a GPU to reduce the spatial query processing cost. Experimental evaluation shows that their hardware-based approach can significantly outperform leading software-based techniques. (Hardware Acceleration for Spatial Selections and Joins Chengyu Sun, Divyakant Agrawal, Amr El Abbadi. Proceedings of SIGMOD 2003.) However, this evaluation is done in a stand-alone setting where there are no indices, preprocessing or other optimizations available in a database. Bandi et al. extend Sun et al.'s work and integrate the hardware-based technique into a popular commercial database. Rigorous experimentation over real-life data sets shows that the hardware-based approach is very effective and can be complimentary to the optimizations available in a commercial database setting. (Hardware Acceleration in Commercial Databases: A Case Study of Spatial Operations Nagender Bandi, Chengyu Sun, Divyakant Agrawal, Amr El Abbadi to appear in VLDB 2004.)