This paper by Dominik Göddeke, Robert Strzodka and Stefan Turek describes a preliminary algorithm to achieve double precision results by adding a CPU-based defect correction to iterative linear system solvers on the GPU. We demonstrate that identical accuracy as compared to a full CPU double precision solver is possible while still gaining a factor of 2 in speedup compared to a highly tuned cache-aware CPU reference implementation in double precision. (Accelerating Double Precision FEM Simulations with GPUs. Dominik Göddeke, Robert Strzodka and Stefan Turek. To appear in Proceedings of ASIM 2005 – 18th Symposium on Simulation Technique.)

This enthusiastic bit of reporting from APC Magazine provides a wild ride through the nascent field of GPGPU. (Hijacking the GPU, by Dan Warne. APC Magazine. August 11, 2005.)

We almost didn’t notice, but when the renewal notice for our domain arrived it pointed out that on August 1st, 2005 GPGPU.org turned 2 years old! To celebrate, we’ve added a wiki, and a few of the regulars on the forums have started The Official GPGPU FAQ. Give it a look.

Caustics are complex patterns of shimmering light formed due to reflective and refractive objects; for example, those formed on the floor of a swimming pool. Caustics Mapping is a physically based real-time caustics rendering algorithm. It utilizes the concept of backward ray-tracing, however it involves no expensive computations that are generally associated with ray-tracing and other such techniques. The main advantage of caustics mapping is that it is extremely practical for games and other interactive applications because of its high frame rates. Furthermore, the algorithm runs entirely on graphics hardware, which leaves the CPU free for other computation. There is no pre-computation involved, and therefore fully dynamic geometry, lighting, and viewing directions are supported. In addition, there is no limitation on the topology of the reciever geometry, i.e., caustics can be formed on arbitrary surfaces. (Caustics Mapping: An Image-space Technique for Real-time Caustics. Musawir A. Shah and Sumanta Pattanaik. Technical Report, School of Engineering and Computer Science, University of Central Florida, CS TR 50-07, 07/29/2005 (Submitted for Publication))

The latest versions of Cycling ’74s MAX/MSP/Jitter software packages provide a visual programming environment for new media with applications in GPU based stream processing, real-time video processing, volume visualization, and generic n-dimensional data analysis and signal processing. Jitter supports cascaded GLSL/Cg/ARB/NV shader programs with a streamlined render-to-texture interface, allowing fast prototyping of complex shader effects to be processed in a generic data flow network. (Jitter v1.5 Upgrade Info. Cycling ’74.)

Many current and upcoming architectures offering large amounts of computational power are designed with data-parallel execution and streaming in mind. We present a streaming algorithm for evaluating an HMM’s Viterbi probability and refine it for the specific HMM used in biological sequence search. We implement our streaming algorithm in the Brook language, allowing us to execute the algorithm on graphics processors. We demonstrate that this streaming algorithm on graphics processors can outperform available CPU implementations. We also demonstrate this implementation running on a 16-node graphics cluster. (ClawHMMer: A Streaming HMMer-Search Implementation. Daniel Horn, Mike Houston, and Pat Hanrahan. Proceedings of Supercomputing 2005.)

To implement dynamic LOD on the GPU, a quadtree structure is created based on a seamless geometry image atlas,and all the nodes in the quadtree are packed into the atlas textures. There are two passes in the approach. In the first pass, the LOD selection is performed in fragment shaders. The resultant buffer is taken as the input texture to the second pass by vertex texturing, and node culling and triangulation are performed in vertex shaders. The LOD algorithm can generate adaptive meshes dynamically, and can be fully implemented on the GPU. It improves the efficiency of LOD selection, and reduces computing load on CPU. (Dynamic LOD on GPU. Junfeng Ji, Enhua Wu, Sheng Li, and Xuehui Liu. Proceedings of Computer Graphics International 2005.)

This paper presents a novel algorithm for solving dense linear systems using graphics processors (GPUs). It reduces matrix decomposition and row operations to a series of rasterization problems on the GPU. These include new techniques for streaming index pairs, swapping rows and columns and parallelizing the computation to utilize multiple vertex and fragment processors. The paper describes implementation of the algorithm on different GPUs and compares the performance with optimized CPU implementations. In particular, implementation on an NVIDIA GeForce 7800 GTX GPU outperforms a CPU-based ATLAS implementation. Moreover, the results show that the algorithm is cache and bandwidth efficient and scales well with the number of fragment processors within the GPU and the core GPU clock rate. The algorithm is demonstrated in the context of fluid flow simulation. (LU-GPU: Efficient Algorithms for Solving Dense Linear Systems on Graphics Hardware To appear in Proceedings of the 2005 ACM/IEEE Super Computing Conference. November 12-18, 2005.)

This IEEE Visualization 2005 paper (accepted for publication) describes a new algorithm for the illustrative rendering of iso-surfaces and polygonal models. Using a combination of multi-pass rendering and image-space processing passes, hidden structures and optional additional inner geometry are displayed in real-time. No pre-processing of the geometric models is necessary. This work is part of Jan Fischer’s PhD thesis. (Illustrative Display of Hidden Iso-Surface Structures, Jan Fischer et al., IEEE Visualization 2005)

Sudipto Guha, Shankar Krishnan and Suresh Venkatasubramanian are presenting a tutorial on the use of the GPU for data visualization and mining at the ACM International Conference on Knowledge Discovery and Data Mining (KDD 2005). (Data Visualization and Mining on the GPU)