The MOSIX group announces the release of the MOSIX Virtual OpenCL (VCL) cluster platform version 1.0, which allows OpenCL applications to transparently utilize many GPU devices in clusters. In the VCL run-time environment, all the cluster devices are seen as if they are located in each hosting-node. Applications need not be aware which nodes and devices are available and where the devices are located. VCL benefits OpenCL applications that can use multiple devices concurrently. Read the rest of this entry »

A webinar by Jack Pappas, CEO and Co-Founder of Tidepowerd is being hosted by NVIDIA this coming Wednesday at 9am PST.

Tidepowerd have created GPU.NET, a software tool which allows developers to write GPU-accelerated code in managed languages like C# and VB.NET.

The “Beta 2″ version of GPU.NET, a new product by TidePowerd, has recently been released. It allows developers to write  GPU-based code in C# or other .NET-supported languages. GPU.NET beta is available for public download, and the full documentation and several example projects are available online.

MAGMA 1.0 RC1 is now available, including the MAGMA sources. MAGMA 1.0 RC1 is intended for a single CUDA enabled NVIDIA GPU. It extends version 0.2 by adding support for Fermi GPUs (see the sample performances for LU, QR, and Cholesky).

Included are routines for the following algorithms:

• LU, QR, and Cholesky factorizations in both real and complex arithmetic (single and double);
• Linear solvers based on LU, QR, and Cholesky in both real and complex arithmetic (single and double);
• Mixed-precision iterative refinement solvers based on LU, QR, and Cholesky in both real and complex arithmetic;
• MAGMA BLAS in real arithmetic (single and double), including gemm, gemv, symv, and trsm.

See the MAGMA homepage for a download link.

The OpenFOAM SpeedIT plugin version 1.1 has been released under the GPL License. The most important new features are:

• Multi-GPU support
• Tested on Fermi architecture (GTX460 and Tesla C2050)
• Automated submission of the domain to the GPU cards (using decomposePar from OpenFOAM)
• Optimized submission of computational tasks to the best GPU card in the system for any number of computational threads
• Plugin picks the most powerful GPU card for a single thread cases

The OpenFOAM SpeedIT plugin is available at http://speedit.vratis.com.

A new major release of rCUDA™ (Remote CUDA), the Open Source package that allows performing CUDA calls to remote GPUs, has been released. The major improvements included in the new version are:

• Updated API to 3.1
• Server now uses Runtime API when possible (CUDA >= 3.1 required)
• Introduced support for the most common CUBLAS routines
• Fixed some bugs
• Added AF_UNIX sockets support to enhance performance on local executions
• Added some load balancing capabilities to the server
• General performance improvements
• Officially added Fermi support

Further information is available from the rCUDA™ webpages http://www.gap.upv.es/rCUDA and http://www.hpca.uji.es/rCUDA.

CUDA 3.2 has been released and can be downloaded from http://developer.nvidia.com/object/cuda_3_2_downloads.html. New features include:

New and Improved CUDA Libraries

• CUBLAS performance improved 50% to 300% on Fermi architecture GPUs, for matrix multiplication of all datatypes and transpose variations
  CUFFT performance tuned for radix-3, -5, and -7 transform sizes on Fermi architecture GPUs, now 2x to 10x faster than MKL
• New CUSPARSE library of GPU-accelerated sparse matrix routines for sparse/sparse and dense/sparse operations delivers 5x to 30x faster performance than MKL
• New CURAND library of GPU-accelerated random number generation (RNG) routines, supporting Sobol quasi-random and XORWOW pseudo-random routines at 10x to 20x faster than similar routines in MKL
• H.264 encode/decode libraries now included in the CUDA Toolkit

CUDA Driver & CUDA C Runtime

• Support for new 6GB Quadro and Tesla products
• New support for enabling high performance Tesla Compute Cluster (TCC) mode on Tesla GPUs in Windows desktop workstations Read the rest of this entry »

From a recent announcement:

We are excited to announce the immediate availability of Cluster GPU Instances for Amazon EC2, a new instance type designed to deliver the power of GPU processing in the cloud. GPUs are increasingly being used to accelerate the performance of many general purpose computing problems. However, for many organizations, GPU processing has been out of reach due to the unique infrastructural challenges and high cost of the technology. Amazon Cluster GPU Instances remove this barrier by providing developers and businesses immediate access to the highly tuned compute performance of GPUs with no upfront investment or long-term commitment.

Learn more about the new Cluster GPU instances for Amazon EC2 and their use in running HPC applications.

Also, community support is becoming available; see for instance this blog post about  SCG-Ruby on EC2 instances.

All talks from the 2010 GPU Technology Conference (as well as archived presentations from GTC 2009) are now available from NVIDIA.

For those who missed this year’s GPU Technology Conference (GTC) , and those who attended, but had a hard time choosing between all the concurrent sessions, NVIDIA has publicly released streamed recordings, video and slides from most  GTC sessions.

There is content available for all types of programmers and developers. Those just getting started programming GPUs may want to take a look at the pre-conference tutorials, which provide an in-depth look at topics such as CUDA C, OpenCL, OpenGL and Parallel Nsight.

From a recent press release:

GPU Systems releases Matlab language bindings for Libra SDK – heterogenous compute platform. Libra 1.2 version with runtime compiler and environment supports x86/x64 backends, OpenGL, OpenCL and CUDA compute backends. This release brings full BLAS 1,2,3 matrix/vector, dense/sparse, real/complex, single/double math library and extended functionality to Matlab computing platform executing on x86 CPUs & GPUs from AMD and NVIDIA.

Examples:

• Matlab Demo running Libra SDK WaterDroplets demo on AMD GPU (youtube video)
• Demo Source Code – matlab version
• Demo Source Code – c++ version