- The multi pass shader partitioning problem was defined in a pair of papers by Chan et al [1,2]. The first successful solution to this problem was presented by Riffel et al . The current paper presents another solution based on dynamic programming and argues that it is scalable . After publication of the paper errors were found in the data presented to support this argument. Nonetheless the algorithm is semi-scalable as claimed. Dynamic Programming is commonly used for instruction selection and this problem is an instance of instruction selection. If scalability failed on this problem it would also fail on instruction selection.
 Proceedings of Graphics Hardware (2004). Efficient Partitioning of Fragment Shaders for Multiple-Output Hardware. Tim Foley, Mike Houston and Pat Hanrahan.
 Proceedings of Graphics Hardware (2004). Mio: Fast Multipass Partitioning via Priority-Based Instruction Scheduling. Andrew Riffel, Aaron E. Lefohn, Kiril Vidimce, Mark Leone, and John D. Owens.
 Proceedings of Graphics Hardware (2005). Optimal Automatic Multi-pass Shader Partitioning by Dynamic Programming. A. Heirich. Click here for slides of the presentation.
This paper gives a fundamental formula for predicting workload imbalance as a result of static load balancing strategies like tiling and randomization in parallel ray tracing. The results can equally be applied to any parallel algorithm for graphics or image processing. It predicts that these strategies will fail at large numbers of computers, and for NTSC resolution images this was true at 128-way parallelism and above. The solution to this failure is dynamic load balancing such as the diffusion strategy in [6,7].
 The Journal of Supercomputing. A Competitive Analysis of Load Balancing Strategies for Parallel Ray Tracing. A. Heirich and J. Arvo, vol. 12, no. 1/2, pp. 57-68 (1998).
 The International Journal for Foundations of Computer Science (1997). A Scalable Diffusion Algorithm for Dynamic Mapping and Load Balancing on Networks of Arbitrary Topology. A. Heirich, vol. 8, no. 3, September 1997, pp. 329-346.
 In proceedings of the International Conference on Parallel Processing (1995). A Parabolic Load Balancing Method. A. Heirich and S. Taylor, vol. III, pp. 192-202. Winner of "outstanding paper of the year". With thanks to Andrew Conley.
 Analysis of scalable algorithms for dynamic load balancing and mapping with application to photo-realistic rendering. (Dissertation)
These papers describe work at HP/Compaq to build a commodity based scalable graphics architecture. The results include world record setting performance and scalability on volume rendering  and a commercial product the HP Scalable Visualization Array. A similar project was developed by Stoll et al  called Lightning-2. Although it was intended as a sort-last architecture Lightning-2 was not scalable and could not support volume rendering or applications that require ordered blending.
 Parallel Computing (2003). Distributed Rendering of Interactive Soft Shadows. M. Isard, M. Shand and A. Heirich. Parallel Computing, vol. 29, no. 3, March 2003, pp. 311-323.
 IEEE Visualization 2002. Workshop on commodity-based visualization clusters (presentation October 27, 2002). Alpha/Depth Acquisition Through DVI. A. Heirich, M. Shand, E. Oertli, G. Lupton and P. Ezolt.
 IEEE Parallel and Large-Data Visualization and Graphics Symposium (2001). Scalable Interactive Volume Rendering Using Off-the-Shelf Components. S. Lombeyda, L. Moll, M. Shand, D. Breen and A. Heirich.
 IEEE Parallel Visualization and Graphics Symposium (1999). Scalable Distributed Visualization Using Off-the-Shelf Components. A. Heirich and L. Moll.
 IEEE Symposium on Field Programmable Custom Computing Machines (1999). Sepia: Scalable 3D Compositing Using PCI Pamette. L. Moll, A. Heirich, and M. Shand.
 In Proceedings of ACM SIGGRAPH (2001). Lightning-2: a high-performance display subsystem for PC clusters. G. Stoll, M. Eldridge, D. Patterson, A. Webb, S. Berman, R. Levy, C. Caywood, M. Taveira, S. Hunt and P. Hanrahan.
 Scalability in large-data scientific visualization. A. Heirich. Pittsburgh Supercomputing Center (2002).
- with Maneesh Agrawala, Ravi Ramamoorthi and Laurent Moll.
 Proceedings of ACM SIGGRAPH (2000). Efficient image-based methods for rendering soft shadows. M. Agrawala, R. Ramamoorthi, L. Moll and A. Heirich.