From a press release:

New Software Solution Reduces Dependency on CPUs

PORTLAND, Ore.- SC09-Nov. 18, 2009- NVIDIA Corporation (Nasdaq: NVDA) and Mellanox Technologies Ltd. today introduced new software that will increase cluster application performance by as much as 30% by reducing the latency that occurs when communicating over Mellanox InfiniBand to servers equipped with NVIDIA Tesla™ GPUs.

The system architecture of a GPU-CPU server requires the CPU to initiate and manage memory transfers between the GPU and the InfiniBand network. The new software solution will enable Tesla GPUs to transfer data to pinned system memory that a Mellanox InfiniBand solution is able to read and transmit over the network. The result is increased overall system performance and efficiency.

“NVIDIA Tesla GPUs deliver large increases in performance across each node in a cluster, but in our production runs on TSUBAME 1 we have found that network communication becomes a bottleneck when using multiple GPUs,” said Prof. Satoshi Matsuoka from Tokyo Institute of Technology. “Reducing the dependency on the CPU by using InfiniBand will deliver a major boost in performance in high performance GPU clusters, thanks to the work of NVIDIA and Mellanox, and will further enhance the architectural advances we will make in TSUBAME2.0.” Read the rest of this entry »

On September 30th NVIDIA unveiled its latest GPU architecture, codenamed “Fermi”.  The first Fermi GPUs will contain 512 “CUDA Cores”, capable of more than 8x the double precision floating-point throughput of its predecessor, the GT200 GPU.  The GPU also incorporates error correcting (ECC) memories and caches, a new cache hierarchy, increased shared memory and register file sizes, and the ability to execute C++ programs.

From the press release:

SANTA CLARA, Calif. -Sep. 30, 2009- NVIDIA Corp. today introduced its next generation CUDA™ GPU architecture, codenamed “Fermi”. An entirely new ground-up design, the “Fermi”™ architecture is the foundation for the world’s first computational graphics processing units (GPUs), delivering breakthroughs in both graphics and GPU computing.

“NVIDIA and the Fermi team have taken a giant step towards making GPUs attractive for a broader class of programs,” said Dave Patterson, director Parallel Computing Research Laboratory, U.C. Berkeley and co-author of Computer Architecture: A Quantitative Approach. “I believe history will record Fermi as a significant milestone.”

Presented at the company’s inaugural GPU Technology Conference, in San Jose, California, “Fermi” delivers a feature set that accelerates performance on a wider array of computational applications than ever before. Joining NVIDIA’s press conference was Oak Ridge National Laboratorywho announced plans for a new supercomputer that will use NVIDIA® GPUs based on the “Fermi” architecture. “Fermi” also garnered the support of leading organizations including Bloomberg, Cray, Dell, HP, IBM and Microsoft.

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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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A public beta release of the CUDA-enabled Fortran Compiler from PGI enables programmers to write code in Fortran for NVIDIA CUDA GPUs.  From a press release:

What: NVIDIA today announced that a public beta release of the PGI® CUDA-enabled Fortran compiler is now available. Developed in collaboration with The Portland Group® , it is the first Fortran compiler compatible with NVIDIA® CUDA™ -enabled graphics processing units (GPUs).

A compiler is a software tool that translates applications from the high-level programming languages used by software developers into a binary form a computer can execute.

Why: GPU computing with the CUDA C-compiler has gained significant momentum in the High-Performance Computing (HPC) space as it enables developers to get transformative increases in performance with minimal coding required.

Fortran is particularly well suited to numeric computation and scientific computing and remains widely used in a wide range of applications such as weather modeling, computational fluid dynamics and seismic processing.

Where can I get it?: Read the rest of this entry »

NVIDIA has released public OpenCL GPU Drivers and an OpenCL performance profiler for Windows, available for free download fromthe NVIDIA OpenCL Download Page.  From an NVIDIA press release:

SANTA CLARA, Calif. -Sep. 28, 2009- NVIDIA today released the first public OpenCL conformant GPU drivers for Windows and Linux. In addition to the drivers themselves, NVIDIA has released a powerful performance profiling tool and an OpenCL Best Practices Guide.

NVIDIA was the first to release beta OpenCL GPU drivers to developers in April 2009.This public release is fully conformant with the OpenCL v1.0 specification and supports the OpenCL Images features of the specification that, while optional for other vendors, provides significant performance benefits across many image processing disciplines such as medical imaging, video transcoding applications, machine vision and facial detection.

Leveraging the extensive performance instrumentation in NVIDIA’s OpenCL drivers and hardware performance signals designed into NVIDIA GPUs, the OpenCL Visual Profiler provides developers with insight into performance bottlenecks and opportunities for optimization.

Key features include:

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Tom’s Hardware has published a comprehensive interview with Ian Buck, NVIDIA’s Director of Software for GPU Computing.  In the interview Ian discusses the history of GPGPU (including a referral to our fair site — thanks Ian!) and his work at Stanford on Brook for GPUs.  He then goes on to discuss the development of CUDA and the teams at NVIDIA responsible; OpenCL and tradeoffs between the industry standard API and C for CUDA; and future directions for GPGPU applications.

AMD is now offering a free OpenCL for CPU beta download as part of the ATI Stream SDK v2.0 Beta Program. The beta will help programmers to more easily develop parallel software programs and take further advantage of multi-core x86 CPUs to accelerate software and deliver a better computing experience. AMD has submitted conformance logs from its Microsoft Windows and Linux CPU beta releases to the Khronos Working Group for certification.

The full press release is available here, and the SDK can be downloaded here.

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