Data Visualization and Mining using the GPU

July 29th, 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)


July 29th, 2005

Once again this year ACM SIGGRAPH will feature a full-day course titled “GPGPU: General-Purpose Computing on Graphics Hardware”. The course, organized by Mark Harris of NVIDIA and David Luebke of the University of Virginia, will feature GPGPU experts from industry and academia. The course will discuss core computational building blocks such as sorting, searching, and linear algebra, using case studies ranging from adaptive shadow mapping to database queries and data mining. Particular focus will be given to tools, perils, and tricks of the trade in general-purpose GPU programming. The course has been updated from SIGGRAPH 2004, with all new case studies. (

Evolutionary Computation on GPUs

July 29th, 2005

Genetic Algorithms (GA) comprise a class of evolutionary computation (EC). A difficulty with GA is that the traditional crossover operation introduces order-dependency and hence an increase in rendering passes on SIMD GPUs. To parallelize EC on GPUs, this project proposes to use another class of EC called Evolutionary Programming (EP), which applies mutations locally. The project studies in-depth how to efficiently map an EP algorithm to SIMD GPUs, including a scalable and visualizable genome map, mutation, tournament and selection, and finally convergence visualization. Intensive experiments and careful comparisons are conducted to demonstrate its performance speedup and accuracy. The project also shows that it is conceptually wrong and infeasible to generate high-quality random numbers on the current generation of GPUs and that the low-quality random numbers will lead to poor performance of EC. (K. L. Fok, T. T. Wong, and M. L. Wong, “Evolutionary Computing on Consumer-Level Graphics Hardware”, IEEE Intelligent Systems, and “Parallel Evolutionary Algorithms on Graphics Processing Unit” in Proc. of IEEE Congress on Evolutionary Computation 2005.)

A Survey of General-Purpose Computation on Graphics Hardware

July 1st, 2005

This new report by Owens et al. is a comprehensive survey of the history and state of the art in GPGPU. It describes, summarizes and analyzes the latest research in mapping general-purpose computation to graphics hardware. The report begins with the technical motivations that underlie general-purpose computation on graphics processors (GPGPU) and describe the hardware and software developments that have led to the recent interest in this field. The authors describe the techniques used in mapping general-purpose computation to graphics hardware, and survey and categorize the latest developments in general-purpose application development on graphics hardware. (A Survey of General-Purpose Computation on Graphics Hardware, by John D. Owens, David Luebke, Naga Govindaraju, Mark Harris, Jens Krüger, Aaron E. Lefohn, Timothy J. Purcell. To appear in proceedings of Eurographics 2005, State of the Art Reports.)

High Performance Sorting on a GPU

July 1st, 2005

This paper by Govindaraju et al. describes a cache-efficient bitonic sorting algorithm on GPUs. The algorithm uses the special purpose texture mapping and programmable hardware to sort IEEE 32-bit floating point data including pointers, and has been used to perform stream data mining and relational database queries. Their results indicate a significant performance improvement over prior CPU-based and GPU-based sorting algorithms. ( GPUSORT: A High Performance Sorting Library”. Also see this Tom’s Hardware article)

Initial Experiences Porting a Bioinformatics Application to a Graphics Processor

July 1st, 2005

Bioinformatics applications are one of the most compute-demanding applications today. While traditionally these applications are executed on cluster or dedicated parallel systems, this paper by M. Charalambous, P. Trancoso, and A. Stamatikis at the University of Cyprus and FORTH explores the use of an alternative architecture. The authors focus on exploiting the characteristics offered by the graphics processors (GPU) in order to accelerate a bioinformatics application. This paper presents the initial results on porting RAxML, a bioinformatics program for phylogenetic tree inference, to the GPU. (Initial Experiences Porting a Bioinformatics Application to a Graphics Processor. M. Charalambous, P. Trancoso, and A. Stamatakis. Proceedings of the 10th Panhellenic Conference in Informatics (PCI 2005))

gDEBugger V1.5 Adds Shaders Source Code Viewer and Supports Multithreaded Applications

July 1st, 2005

gDEBugger, an OpenGL debugger and profiler, traces application activity on top of the OpenGL API letting programmers see what is happening within the graphic system implementation. The new V1.5 introduces a Shader Viewer that displays a list of shading programs and shaders existing in each render context. This viewer displays each shader’s source code and parameters. Also displayed is a list of each program’s attached shaders, active uniforms values and program parameters. In addition, this version supports multithreaded applications, displaying a list of the debugged process threads and thread current render contexts. The Call Stack View now displays the call stack of any chosen thread. (

Sh Version 0.7.8 Released

July 1st, 2005

A new version of the Sh language for GPU programming in C++ has been released. This version features a new backend infrastructure implementation allowing such things as running part of a stream application on the GPU and part on the CPU at the same time. Many other fixes as well as platform compatability enhancements were also added. (

Radiance Cache Splatting: A GPU-Friendly GLobal Illumination Algorithm

June 14th, 2005

The irradiance caching algorithm is commonly used for fast global illumination since it provides high-quality rendering in a reasonable time. However this algorithm relies on a spatial data structure along with complex algorithms. This central and permanently modified data structure prevents this algorithm from being easily implemented on GPUs. This paper proposes a novel approach to global illumination using irradiance and radiance cache: the Radiance Cache Splatting. This method directly meets the processing constraints of graphics hardware since it avoids the need of complex data structure and algorithms. Moreover, the rendering quality remains identical to classical irradiance and radiance caching. This work will be presented at the Eurographics Symposium on Rendering 2005, and during SIGGRAPH 2005 sketches. (Radiance Cache Splatting: A GPU-Friendly GLobal Illumination Algorithm. Pascal Gautron, Jaroslav Krivanek, Kadi Bouatouch, Sumanta Pattanaik. Proceedings of Eurographics Symposium on Rendering 2005)

Exploring Graphics Processor Performance for General Purpose Applications

June 12th, 2005

This paper by P. Trancoso and M. Charalambous at the University of Cyprus presents a comprehensive study of the performance of general-purpose applications on the GPU, and determines the conditions that make the GPU work efficiently. Also, as the GPU is cheaper and consumes less power than a high-end CPU, the authors show the benefits of using the graphics card to extend the life-time of an existing computer system. (Exploring Graphics Processor Performance for General Purpose Applications. P. Trancoso and M. Charalambous. Proceedings of the Eighth Euromicro Conference on Digital System Design (DSD 2005))

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