Neoptica has recently posted a whitepaper, “Programmable Graphics—The Future of Interactive Rendering.” It introduces the coming era of programmable graphics, in which developers implement rendering algorithms using combinations of parallel CPU and GPU tasks executing cooperatively on heterogeneous multi-core architectures of the near future. By embracing both task- and data-parallel computation, this approach frees developers to use the most efficient parallel computation style for their algorithms, and makes it possible to define custom graphics pipelines built using complex algorithms and dynamic data structures. The paper argues that future graphics applications that leverage the tightly coupled capabilities of forthcoming CPUs and GPUs will generate far richer and more realistic imagery, use computational resources more efficiently, and scale to large numbers of CPU and GPU cores.

A beta of NVIDIA’s CUDA development environment, NVIDIA’s new technology for computing with GPUs, is now posted on developer.nvidia.com. This beta release of CUDA contains a C compiler for the GPU and an SDK with examples to get you started coding for the GPU. From the press release:

GPU Computing with CUDA is a new approach to computing where hundreds of on-chip processors simultaneously communicate and cooperate to solve complex computing problems. Applications that require mathematically intensive computing on large amounts of data are ideal targets for GPU Computing. NVIDIA NVIDIA’s CUDA technology is available in GeForce 8800 graphics products and future NVIDIA Quadro Professional Graphics solutions based on 8-series (G8X) GPUs. Developers are invited to download the beta version of the CUDA Software Developers Kit (SDK) and C compiler for Windows XP and Linux (RedHat Release 4 Update 3) from the NVIDIA Developer Web site at developer.nvidia.com/cuda. GPU Computing Forums for news, discussion and programming tips are also available at forums.nvidia.com.

This article at HPC Wire by Matthew Papakipos, CTO of PeakStream Technologies, discusses the convergence of CPU and GPU architectures, the programming challenges architecture changes pose, and possible solutions to these challenges.

NVIDIA Corporation today unveiled NVIDIA CUDA technology, a new architecture for computing on NVIDIA GPUs, and the industry’s first C-compiler development environment for the GPU. From the NVIDIA Press Release:

GPU computing with CUDA is a new approach to computing where hundreds of on-chip processor cores simultaneously communicate and cooperate to solve complex computing problems up to 100 times faster than traditional approaches. This breakthrough architecture is complemented by another first: the NVIDIA C-compiler for the GPU. This complete development environment gives developers the tools they need to solve new problems in computation-intensive applications such as product design, data analysis, technical computing, and game physics. CUDA-enabled GPUs offer dedicated features for computing, including the Parallel Data Cache, which allows 128, 1.35 GHz processor cores in newest generation NVIDIA GPUs to cooperate with each other while performing intricate computations. Developers access these new features through a separate computing driver that communicates with DirectX and OpenGL, and the new NVIDIA C compiler for the GPU, which obsoletes streaming languages for GPU computing.

CUDA website: http://www.nvidia.com/cuda

This white paper from RapidMind and HP compares the performance of BLAS dense linear algebra operations, the FFT, and European option pricing on the GPU against highly tuned CPU implementations on the fastest available CPUs. All of the GPU implementations were made using the RapidMind Development Platform, which allows the use of standard C++ programming to create high-performance parallel applications that run on the GPU. The full source for the samples is available in conjunction with a new beta version of the RapidMind development platform. The results will also be presented as a poster at SC06.
(http://rapidmind.net/sc06_hp_rapidmind_cpugpu_summary.php)

PeakStream, Inc., a leading software application platform provider for the high performance computing (HPC) market, today unveiled the PeakStream Platform. Available immediately, the PeakStream Platform makes it possible to easily program new high performance processors such as multi-core CPUs, graphics processing units (GPUs) and Cell processors, converting them into radically powerful computing engines for exponentially increased application performance and decreased time-to-solution at reduced cost. The company also announced the completion of equity financing totaling $17 million from Kleiner Perkins Caufield & Byers, Sequoia Capital and Foundation Capital. (www.peakstreaminc.com)

EM Photonics, Inc., a leading provider of accelerated hardware technologies, released FastFDTD, a free 2D and 3D accelerated FDTD solver based on GPU technology. The FastFDTD toolkit contains all files and documentation necessary to accelerate FDTD computations using a simple input file format. The 2D and 3D solvers include a variety of sources and materials, and more are being added. When asked why EM Photonics was providing this toolkit for free, Eric Kelmelis, Vice President, said

We decided to release our GPU-based FDTD accelerator free of charge to demonstrate the power of application acceleration with alternative computational platforms. This solver shows a single graphics card running 20-30 times faster than an optimized software implementation. Our focus will remain on pushing the boundaries of this technology and accelerating other applications with commodity hardware devices such as graphics cards and FPGAs.

For more information, including specific feature sets, compatible graphics cards, and detailed license information, please visit the FastFDTD webpage at http://www.emphotonics.com/fastfdtd.html

Geomerics, a new R&D company based in Cambridge UK, have recently announced a real-time radiosity simulation running entirely on the GPU. The solution runs at up to 100hz on common graphics hardware and allows for fully dynamic lighting, including spot-lights, projected texture or video lighting, and area lights. It integrates well with traditional modeling techniques such as normal mapping, and all lighting is performed in high dynamic range. Videos, screen shots and further details of the simulation can be found on the  Geomerics website.

Fantasy Lab, a game developer located in the San Francisco Bay area, has announced its new game engine, which includes support for real-time global illumination and displacement-mapped subdivision surfaces. Videos on the company’s website show global illumination on an animated subdivision-surface-based character. The global illumination solution for the videos is calculated in 3.3 milliseconds per frame (300 frames per second) on an NVIDIA GeForce Go 7900 GTX (a laptop GPU).

At GDC 2006 in San Jose next week Havok will announce Havok FX, a game physics framework for GPUs. There are two talks about Havok FX:

Havok FX: GPU-accelerated Physics for PC Games
Speaker: Andrew Bond (Havok)
This session introduces Havok’s latest innovation for game physics: Havok FX, which enables real-time processing of thousands of rigid-body objects on current and next generation GPUs. Havok’s general approach to GPU Effects Physics will be covered, as well as tool-chain requirements and trade-offs with game-critical, game-play physics processing on the CPU.

Physics Simulation on NVIDIA GPUs
Speakers: Simon Green, Mark Harris (NVIDIA)
Havok FX leverages state of the art software and hardware technology from NVIDIA to extend the capabilities of NVIDIA GPUs and SLI multi-GPU systems to include physics processing for massive real-time effects. In this presentation NVIDIA and Havok engineers will describe how Havok FX utilizes NVIDIA technology to simulate and render thousands of particles and rigid bodies in games. Live real-time demos will demonstrate the high performance available with current GPUs and provide a look into the future of physics processing on NVIDIA GPUs.