At SIGGRAPH 2009 , the Khronos Group announced OpenGL 3.2, the third major update in twelve months. The most important new features are GLSL version 1.5 with core support for geometry shaders, and the introduction of two profiles within the specification providing developers the choice of using the streamlined Core profile for new application development or the Compatibility profile which provides full backwards compatibility with previous versions of the OpenGL standard for existing and workstation applications.

The full press release is here,  and the specification is available for download.

A ScientificComputing.com article by Rob Farber explores the topic of numerical precision in the context of future exascale computing, asking the question “how do we know that anything we compute is correct?”  The discussion centers around processors such as GPUs which provide both single- and double-precision computation but at different throughput levels. “Taking a multi-precision approach can enhance the accuracy of a calculation and justify the use of mainly single-precision arithmetic (for performance) along with the occasional use of double-precision (64-bit) arithmetic for precision-sensitive operations,” writes Farber. (Rob Farber. “Numerical Precision: How Much is Enough?” ScientificComputing.com.  Accessed July 1, 2008.)

From an NVIDIA Press Release:

SANTA CLARA, CA—APRIL 20, 2009—NVIDIA Corporation, the inventor of the GPU, today announced the release of its OpenCL driver and software development kit (SDK) to developers participating in its OpenCL Early Access Program. NVIDIA is providing this release to solicit early feedback in advance of a beta release which will be made available to all GPU Computing Registered Developers in the coming months.

Developers can apply to become a GPU Computing Registered Developer at: www.nvidia.com/opencl

“The OpenCL standard was developed on NVIDIA GPUs and NVIDIA was the first company to demonstrate OpenCL code running on a GPU,” said Tony Tamasi, senior vice president of technology and content at NVIDIA. “Being the first to release an OpenCL driver to developers cements NVIDIA’s leadership in GPU Computing and is another key milestone in our ongoing strategy to make the GPU the soul of the modern PC.”

At the core of NVIDIA®’s GPU Computing strategy is the massively parallel CUDA™ architecture that NVIDIA pioneered and has been shipping since 2006. Accessible today through familiar industry standard programming environments such as C, Java, Fortran and Python, the CUDA architecture supports all manner of computational interfaces and, as such, is a perfect complement to OpenCL. Enabled on over 100 million NVIDIA GPUs, the CUDA architecture is enabling developers to innovate with the GPU and unleash never before seen performance across a wide range of applications.

Developers can apply to become a GPU Computing Registered Developer at: www.nvidia.com/opencl

Graphic Remedy launched the first official version of gDEBugger Mac at this year’s Game Developers Conference, held in San Francisco, 23-27 March. On Tuesday March 24, gDEBugger Mac was demonstrated in the Khronos Developer University full-day tutorial area. A fully functional trial version of gDEBugger Mac is now available for download.

gDEBugger is an OpenGL Debugger and Profiler. It traces application activity on top of the OpenGL API, lets programmers see what is happening within the graphics system implementation to find bugs and optimize OpenGL application performance.

gDEBugger Mac brings all of gDEBugger’s Debugging and Profiling abilities to the Mac OS X OpenGL developer’s world. gDEBugger now runs on Windows, Mac OS X and Linux operating systems.

In this ClusterMonkey article, Andrew Humber, Senior PR Manager for Tesla and CUDA Technologies at NVIDIA Corporation, summarizes the events that made 2008 a truly exciting year for GPU Computing. (A Year in Review from the NVIDIA Tesla Team, ClusterMonkey)

This article by Jeff Layton at ClusterMonkey summarizes the history of GPU Computing in terms of high-level programming languages and abstractions, from the early days of GPGPU programming using graphics APIs, to Stream, CUDA and OpenCL. The second half of the article provides an introduction to the PGI 8.0 Technology Preview, which allows the use of pragmas to automatically parallelize and run compute-intensive kernels in standard C and Fortran code on accelerators like GPUs. (GPU Programming For the Rest Of Us, Jeff Layton, ClusterMonkey.net)

OpenMM is a freely downloadable, high performance, extensible library that allows molecular dynamics (MD) simulations to run on high performance computer architectures, such as graphics processing units (GPUs). Significant performance speedups of 100 times were achieved in some cases by running OpenMM on GPUs in desktop PCs (vs CPU). The new release includes a version of the widely used MD package GROMACS that integrates the OpenMM library, enabling acceleration on high-end NVIDIA and AMD/ATI GPUs. OpenMM is a collaborative project between Vijay Pande’s lab at Stanford University and Simbios, the National Center for Physics-based Simulation of Biological Structures at Stanford, which is supported by the National Institutes of Health. For more information on OpenMM, go to http://simtk.org/home/openmm. (Full press release.)

The University of Illinois at Urbana-Champaign is launching a 13-week seminar series that will focus on emerging applications for parallel computing. The Need for Speed Seminar Series will feature world-class applications experts and researchers who will discuss what increased computing performance means for their fields. The series will bring together hardware engineers and software developers who require parallel processing to create faster and superior applications. Speakers will help forecast breakthroughs enabled by the rapid advances in computing performance per dollar, performance per watt, or storage capacity provided by Moore’s Law.

David Kirk, NVIDIA Fellow, will kick off the series with a special keynote on January 28. Following that, the Need for Speed series will be held at 4pm CT every Wednesday until April 29 at the UI’s Coordinated Science Laboratory. Seminars will also stream live over the internet and speakers will take questions from both in-house and online audience members. To learn more about the series, or to view the live seminars, please visit the Need for Speed seminar web page.

(Editor’s Note: this news was submitted after the talk occurred.)

NVIDIA announced that National Taiwan University has been named as Asia’s first CUDA Center of Excellence (press release below). The university earned this title by formally adopting NVIDIA GPU Computing solutions across its research facilities and integrating a class to teach parallel computing based on the CUDA architecture into its educational curriculum. As the computing industry rapidly moves toward parallel processing and many-core architectures, over the past year, NVIDIA has worked to offer tomorrow’s developers and engineers education on the best tools and methodologies for parallel computing. In addition to working with over 50 Universities worldwide that are actively using CUDA in their courses, NVIDIA developed the CUDA Center of Excellence Program to further assist universities that are devoted to educating tomorrow’s software developers about parallel computing. (Press Release)

From a press release:

SANTA CLARA, CA—JANUARY 15, 2009—NVIDIA today announced it is now working closely with Wipro to provide CUDA™ professional services to their joint customers worldwide. CUDA, NVIDIA’s parallel computing architecture accessible through an industry standard C language programming environment, has already delivered major leaps in performance across many industries. Wipro’s Product Engineering Services group will accelerate the development efforts of companies with vast software portfolios seeking to exploit parallel computing with the GPU.

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