From a recent press release:
Taipei, November 18, 2011: Zillians, a leading cloud solution provider specializing in high performance computing, GPU virtualization middleware and massive multi-player online game (MMOG) platforms today announced the availability of vGPU – the world’s first commercial virtualization solution for decoupling GPU hardware from software. Traditionally, physical GPUs must reside on the same machine running GPU code. This severely hampers GPU cloud deployment due to the difficulty of dynamic GPU provisioning. With vGPU technology, bulky hardware is no longer a limiting factor. vGPU introduces a thin, transparent RPC layer between local application and remote GPU, enabling existing GPU software to run without any modification on a remote GPU resource. Read the rest of this entry »
EM Photonics has released CULA Sparse, a ready-to-integrate package for solving sparse linear systems. Features include:
- Interfaces: C, C++, Fortran, Matlab, Python
- Platforms: all CUDA platforms. including Linux, Windows, and OS X
- Solvers and preconditioners: BiCG, BiCGStab, CG, GMRES, MINRES and Jacobi, ILU(0)
- Data formats: COO, CSR, CSC in double precision real and complex floating point
- No CUDA programming experience required.
More information is available at http://www.culatools.com/sparse.
The latest release of Symscape’s Caedium (v3.0) now has support for CFD simulations using NVIDIA CUDA GPU devices on Windows and Linux. Caedium is an integrated simulation environment that targets Computational Fluid Dynamics (CFD). The GPU support is provided by Symscape’s ofgpu linear solver library for OpenFOAM®. For more details see:
The second 2-day CUDA programming workshop in Berlin takes place November 5-6. Course details, outline and prices are available at http://cuda.eventbrite.com.
From the abstract of a GPU market analysis whitepaper by John Peddie Research:
Computer graphics is hard work. Behind the images you see in games and movies, or while editing photos or video, some serious processing is taking place. All the processing power you can muster is needed to push and polish pixels. And this task is only going to get more demanding as these applications get more sophisticated. Graphics Processing Units (GPUs), which do the heavy lifting in computer graphics, range greatly in size, price and performance. They span from tiny cores inside an ARM processor (such as Nvidia’s Tegra or Qualcomm’s Snapdragon), to graphics integrated within an X86 processor (such as AMD’s Fusion, Intel’s Sandy Bridge), to a standalone discrete device, or dGPU (such as AMD’s Radeon, or Nvidia’s GeForce).
More information: http://jonpeddie.com/media/presentations/an-analysis-of-the-gpu-market/
Implementing flexible software solutions, such as rendering and ray tracing, is still challenging for GPU programs. The amount of available memory on modern GPUs is relatively small. Scenes for feature film rendering and visualization have large geometric complexity and can easily contain millions of polygons and a large number of texture maps and other data attributes. CentiLeo presents an interactive out-of-core ray tracing engine running on the single desktop GPU. The system is built around a virtual memory manager. A novel ray intersection algorithm built around an acceleration structure, cached on the GPU, loads needed data on-demand using page swapping. The ray tracing engine is used to implement a variety of rendering and light transport algorithms. The system is implemented using CUDA and runs on a single NVIDIA GTX 480.
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TidePowerd has released Version 2 of their GPU computing solution for the .NET framework, GPU.NET. Their platform allows developers to quickly and easily write GPU-accelerated applications completely in .NET-based languages. Some key benefits include:
- Stay in C# and treat kernel methods like any regular method
- “Boilerplate” GPU programming tasks such as memory transfer and GPU scheduling are abstracted from the developer
- Cross-platform and cross-hardware with a single binary
- Systems seamlessly adapt to new hardware without rewriting code
- Speed on par with native code
New version 2 features:
- Visual Studio Error list and IntelliSense integration
- On-device random number generation
- Double precision support
A free 30-days evaluation license is available, as well as in-depth examples and tutorials.
Jacket 1.8 and LibJacket 1.1 have been released by Accelereyes, enabling GPU support for MATLAB and easier CUDA development with C/C++/Fortran and Python. New features include:
- Expanded support for the Signal Processing, Image Processing, and Statistics Libraries included with both Jacket and LibJacket
- Faster linear algebra for special systems (e.g. symmetric, positive definite, triangular, etc.)
- Enhanced visualizations
- New and updated examples: FDTD, Mandelbrot fractals, maximum-likelihood neural segmentation, MDS for genomics
- Built with CUDA 4.0 for peak performance
Visit http://www.accelereyes.com/ for details, downloads, whitepapers and tutorials.
TunaCode is pleased to announce the release of CUVI (CUDA Vision and Imaging Library) version 0.5 which comes with a new API and new features. This release makes it even simpler to add acceleration to existing Imaging applications, without any prior technical knowledge of GPUs. CUVI v0.5 is built from bottom up with performance and ease-of-use in mind.
CUVI version 0.5 is available for download at http://cuvilib.com and is available for Windows (Win32, x64) with planned support for Linux and Mac.
Microsoft has announced that the next version of Visual Studio will contain technology labeled C++ Accelerated Massive Parallelism (C++ AMP) to enable C++ developers to take advantage of the GPU for computation purposes. More information is available in the MSDN blog posts here and here.