GPGPU |
General-Purpose Computation Using Graphics Hardware
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IntroductionGPGPU stands for General-Purpose computation on GPUs. With the increasing programmability of commodity graphics processing units (GPUs), these chips are capable of performing more than the specific graphics computations for which they were designed. They are now capable coprocessors, and their high speed makes them useful for a variety of applications. The goal of this page is to catalog the current and historical use of GPUs for general-purpose computation.
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A Survey of General-Purpose Computation on Graphics Hardware 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 Krueger, Aaron E. Lefohn, Timothy J. Purcell. To appear in proceedings of Eurographics 2005, State of the Art Reports.)
Posted: 01 Jul 2005 [GPGPU ] # High Performance Sorting on a GPU 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)
Posted: 01 Jul 2005 [GPGPU /Database/Sort & Search] # Initial Experiences Porting a Bioinformatics Application to a Graphics Processor 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))
Posted: 01 Jul 2005 [GPGPU /Med & Bio] # gDEBugger V1.5 Adds Shaders Source Code Viewer and Supports Multithreaded Applications 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. (www.gremedy.com)
Posted: 01 Jul 2005 [GPGPU /Tools] # 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. (http://libsh.org)
Posted: 01 Jul 2005 [GPGPU /High-Level Languages] # |
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