ofgpu is a free GPL library from Symscape that provides GPU linear solvers for OpenFOAM®. The experimental library targets NVIDIA CUDA devices on Windows, Linux, and (untested) Mac OS X. It uses the Cusp library’s Krylov solvers to produce equivalent GPU (CUDA-based) versions of the standard OpenFOAM linear solvers:

• PCG – Preconditioned conjugate gradient solver for symmetric matrices (e.g., p)
• PBiCG – Preconditioned biconjugate gradient solver for asymmetric matrices (e.g., Ux, k)

ofgpu also has support for the OpenFOAM preconditioners:

• no
• diagonal

For more details see “GPU Linear Solver Library for OpenFOAM”. OpenFOAM is a registered trademark of OpenCFD and is unaffiliated with Symscape.

KGPU is a GPU computing framework for the Linux kernel. It allows the Linux kernel to directly execute CUDA programs running on GPUs. The motivation is to augment systems with GPUs so that like user-space applications, the operating system itself can benefit from the GPU acceleration. It can also offload computationally intensive work from the CPU by enabling the GPU as an extra computing device.

The current KGPU release includes a demo task with GPU augmentation: a GPU AES cipher based eCryptfs, which is an encrypted file system on Linux. The read /write bandwidths are expected to be accelerated by a factor of 1.7 ~ 2.5 on an NVIDIA GeForce GTX 480 GPU.

The source code can be obtained from https://github.com/wbsun/kgpu, and news and release information can be found at http://code.google.com/p/kgpu/.

Heterogeneous computing is moving into the mainstream, and a broader range of applications are already on the way. As the provider of world-class CPUs, GPUs, and APUs, AMD offers unique insight into these technologies and how they interoperate. We’ve been working with industry and academia partners to help advance real-world use of these technologies, and to understand the opportunities that lie ahead. It’s time to share what we’ve learned so far.

With tutorials, hands-on labs, and sessions that span a range of topics from HPC to multimedia, you’ll have the opportunity to expand your view of what heterogeneous computing currently offers and where it is going. You’ll hear from industry innovators and academic pioneers who are exploring different ways of approaching problems, and utilizing new paradigms in computing to help identify solutions. You’ll meet AMD experts with deep knowledge of hardware architectures and the software techniques that best leverage those platforms. And you’ll connect with other software professionals who share your passion for the future of technology.

Learn more at developer.amd.com/afds.

The call for Submissions for GPU Technology Conference 2011 (GTC), October 11-14, is now open.  You can find more details and instructions for submitting here.

Expanding the already comprehensive breadth of topics covered at GTC 2010, the GTC Content Committee has added new topic areas for 2011. Below is a partial list; see the GTC website for full details:

• Application Design & Porting Techniques
• Bioinformatics
• Climate & Weather Modeling
• Cluster Management
• Computational Structural Mechanics
• Parallel Programming Languages
• Supercomputing

GTC is also looking for posters that describe novel or interesting research topics in parallel computing, visual computing, and applications of GPUs, with a particular interest in submissions describing GPU computing and CUDA applications that solve diverse problems in scientific and engineering domains. Read the rest of this entry »

From a recent press release:

CUDAfy is a .NET SDK that allows you to write, debug and emulate CUDA GPU applications in any .NET language including C# or Visual Basic. The aim is to bring the power of GPGPU to the large number of .NET developers out there. Features include:

• .NET object orientated CUDA model (GThread)
• Write .NET code marking methods, structures and constants that should be translated to CUDA (“Cudafying”)
• An add-in for Red Gate’s .NET Reflector tool that translates to CUDA C
• Built in emulation of GPU kernel functions
• 1D, 2D and 3D array support including access to Array class’s Length, GetLength and Rank members
• Use all standard .NET value types. No new types even for managing data allocated on GPU
• Simple .NET wrapper for CUFFT and CUBLAS

During our work with the European Space Agency, Astrium and NLR we saw how GPUs could significantly improve performance of the emulation of algorithms targeted on FPGAs and ASICs. The SDEs and SDKs produced were .NET based and CUDAfy is the result of efforts to more tightly integrate the GPU and CPU code development. There are user guides and sample projects. Many of the samples in the book CUDA by Example have been ported to .NET. See www.hybriddsp.com for downloads and more information.

OpenCL Studio combines OpenCL and OpenGL into a single integrated development environment for high performance computing. The feature rich editor, interactive scripting language and extensible plug-in architecture support the rapid development of complex parallel algorithms and accompanying visualization. The first production version of OpenCL Studio including instructional videos and demo applications are available at www.opencldev.com.

AccelerEyes has released version 1.7 of Jacket for GPU computing with MATLAB®. Version 1.7 delivers even more speed to MATLAB with a new Sparse Linear Algebra Library, a new Signal Processing Library, a big boost to convolution functions, and more.

Jacket is the premier GPU software plugin for MATLAB. It enables rapid prototyping and problem solving across a range of government, manufacturing, energy, media, biomedical, financial, and scientific research applications. Jacket accelerates performance of common arithmetic and linear algebra functionality using the complete line of CUDA-capable GPUs from NVIDIA, including top of the line Tesla GPUs as well as Quadro visualization GPUs and GeForce gaming GPUs.

Some of the new features available with Jacket 1.7 include:

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Exact String Matching algorithms are heavily used in a lot of applications like antivirus engines, DNA sequencing, text editors etc. This project provides CUDA implementations of  the naive, horspool and quicksearch algorithms, including a performance comparison against CPU versions: http://code.google.com/p/exactstrmatchgpu.

Today NVIDIA announced the upcoming 4.0 release of CUDA.  While most of the major CUDA releases accompanied a new GPU architecture, 4.0 is a software-only release, but that doesn’t mean there aren’t a lot of new features.  With this release, NVIDIA is aiming to lower the barrier to entry to parallel programming on GPUs, with new features including easier multi-GPU programming, a unified virtual memory address space, the powerful Thrust C++ template library, and automatic performance analysis in the Visual Profiler tool.  Full details follow in the quoted press release below.

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PFAC, the Parallel Failureless Aho-Corasick algorithm is a variant of the well-known Aho-Corasick (AC) algorithm with all failure transitions removed. The purpose of PFAC is to match all longest patterns in a given input stream against patterns pre-defined by users. The data-parallel nature of PFAC makes it perform well on GPUs, especially NVIDIA Fermi-based GPUs. The PFAC library, implemented in CUDA, provides a C level API that is easy to use. Users need not know CUDA programming. The user guide provides simple example to make it easy to use PFAC for content searches or virus detection on the GPU.

The PFAC library does not use multiple GPUs intrinsically but users can combine PFAC library with OpenMP or PThreads libraries to perform string matching on Multiple GPUs. The PFAC release includes OpenMP and PThreads examples. Download and further information: http://code.google.com/p/pfac/