Graphic Remedy is proud to announce the upcoming release of gDEBugger for OpenCL on Windows, Mac OS X and Linux. This new product will bring gDEBugger’s advanced Debugging, Profiling and Memory Analysis abilities to the OpenCL developer’s world, helping OpenCL developers find bugs and optimize parallel computing application performance and memory consumption.

To join the Free Beta Program, see screenshots and more details, please visit http://www.gremedy.com/gDEBuggerCL.php.

gDEBugger CL enables OpenCL developers to:

• Locate parallel computing performance bottlenecks
• Edit and continue OpenCL kernels “on the fly”
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The first textbook of its kind, Programming Massively Parallel Processors: A Hands-on Approach launches today, authored by Dr. David B. Kirk, NVIDIA Fellow and former chief scientist, and Dr. Wen-mei Hwu, who serves at the University of Illinois at Urbana-Champaign as Chair of Electrical and Computer Engineering in the Coordinated Science Laboratory, co-director of the Universal Parallel Computing Research Center and principal investigator of the CUDA Center of Excellence. The textbook, which is 256 pages, is the first aimed at teaching advanced students and professionals the basic concepts of parallel programming and GPU architectures. Published by Morgan-Kauffman, it explores various techniques for constructing parallel programs and reviews numerous case studies.

With conventional CPU-based computing no longer scaling in performance and the world’s computational challenges increasing in complexity, the need for massively parallel processing has never been greater. GPUs have hundreds of cores capable of delivering transformative performance increases across a wide range of computational challenges. The rise of these multi-core architectures has raised the need to teach advanced programmers a new and essential skill: how to program massively parallel processors.

Among the book’s key features:

• First and only text that teaches how to program within a massively parallel environment
• Portions of the NVIDIA-provided content have been part of the curriculum at 300 universities worldwide
• Drafts of sections of the book have been tested and taught by Kirk at the University of Illinois
• Book utilizes OpenCL and CUDA C, the NVIDIA parallel computing language developed specifically for massively parallel environments

Programming Massively Parallel Processors: A Hands-on Approach is available to purchase from Amazon or directly from Elsevier.

From the release notes:

ATI Stream SDK 2.0 is the first production SDK for both AMD GPUs and x86 CPUs. This release supports a wide range of ATI graphics processors, including the new ATI Radeon HD 5970, and provides support for OpenCL ICD (Installable Client Driver), atomic functions for 32-bit integers, a Microsoft Visual Studio 2008-integrated ATI Stream Profiler performance analysis tool, and other robust features. Preview support for upcoming features include OpenCL and Microsoft DirectX 10 interoperability, and double-precision floating point basic arithmetic in OpenCL C kernels.

This web site, maintained by Eric Bainville, presents several example codes, tutorials and low-level benchmarks, using OpenCL to compare multicore CPUs and GPUs. Examples include operations and basic arithmetic throughput, a Mandelbrot renderer and and multiprecision algorithms to multiply million-digit numbers.

Remote Access: GoToMeeting
Date & Time: Thursday, December 10 at 15:00 CST

Abstract:

OpenCL is a new industry standard programming system for developing parallel programs that typically execute on heterogeneous computing systems. OpenCL has much in common with NVIDIA´s CUDA programming toolkit, but differs in a number of important respects as a result of its goal of supporting a broader range of target hardware platforms. This talk will introduce OpenCL and provide some basic comparisons with other programming systems.

Bio: John Stone is a Senior Research Programmer in the Theoretical and Computational Biophysics Group at the Beckman Institute for Advanced Science and Technology. Mr. Stone is the lead developer of VMD, a high performance molecular visualization tool used by researchers all over the world. His research interests include molecular visualization, GPU computing, parallel processing, ray tracing, haptics, and virtual environments.

Click here to register for the webinar.

The 1.0 Beta version of OpenMM has just been released.  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). It currently supports NVIDIA GPUs and provides preliminary support for the new cross-platform, parallel programming standard OpenCL, which will enable it to be used on ATI GPUs.

The new release includes support for Particle Mesh Ewald and custom non-bonded interactions.  In conjunction with this release, a new version of the code needed for accelerating the GROMACS molecular dynamics software using OpenMM is also available.

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, visit http://simtk.org/home/openmm.

These webinars cover many topics including an introduction to C for CUDA, the OpenCL™ API, and performance optimization techniques, presented by NVIDIA DevTech Engineers with additional staff online to answer questions.

Full Schedule and short abstracts can be viewed at: http://developer.nvidia.com/object/gpu_computing_online.html

AMD’s STREAM SDK v2.0 beta4 is the first release of the STREAM SDK with OpenCL support on CPUs and GPUs. The OpenCL implementation is certified OpenCL 1.0 conformant by the Khronos group. Supported platforms are Windows XP, Vista and Windows 7, and a number of Linux distributions, all in 32 and 64-bit. The implementation supports AMD and Intel multicore CPUs, as well as the two latest GPU generations from AMD.

The STREAM SDK as well as documentation and further information is available on AMD’s developer website.

nCore Design announces the immediate availability of the NCT-300 Programming GPU Processors course. Conceived with the experienced C/C++ programmer in mind, NCT-300 covers concepts and approaches related to programming GPU processors using both CUDA and OpenCL. The course covers GPU hardware, memories, data transport, CUDA and OpenCL APIs, programming methods and performance optimization. It will enable students to understand the fundamental aspects of GPU programming and become proficient in a relatively short time. Extensive hands-on laboratories demonstrate how to apply common numerical methods using both native APIs and open source libraries. Other topics covered in the course include integrating the Intel Threading Building Blocks (TBB) abstraction layer with native GPU software APIs in addition to a GPU debugging primer.

The class brochure is available for download. To register, schedule an on-site session or contact nCore Design, go to http://www.ncoredesign.com/company/contact_us.

AMD announced its latest ATI Radeon™ series of graphics cards on September 23rd.  The new GPUs boast up to 2.72 GFLOP/s of single-precision floating point throughput, along with DirectX® 11 graphics (including DirectCompute) and OpenCL 1.0 support.

From the press release:

AMD (NYSE: AMD) today launched the most powerful processor ever created¹, found in its next-generation graphics cards, the ATI Radeon™ HD 5800 series graphics cards, and the world’s first and only to fully support Microsoft DirectX® 11², the new gaming and compute standard shipping shortly with Microsoft Windows® 7operating system. Boasting up to 2.72 TeraFLOPS of compute power, the ATI Radeon™ HD 5800 series effectively doubles the value consumers can expect of their graphics purchases, delivering twice the performance-per-dollar of previous generations of graphics products.³ AMD will initially release two cards: the ATI Radeon HD 5870 and the ATI Radeon HD 5850, each with 1GB GDDR5 memory. With the ATI Radeon™ HD 5800 series of graphics cards, PC users can expand their computing experience with ATI Eyefinity multi-display technology, accelerate their computing experience with ATI Stream technology, and dominate the competition with superior gaming performance and full support of Microsoft DirectX® 11, making it a “must-have” consumer purchase just in time for Microsoft Windows® 7 operating system.

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