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.

From a press release:

AUSTIN, Texas, — Financial institutions are turning to graphics processing unit (GPU) computing for real economic and performance benefits. Fast and accurate derivatives pricing model development and accelerated execution speeds are crucial for today’s derivatives marketplace. SciComp Inc. has enhanced SciFinance®, its flagship derivatives pricing software, to help quantitative developers further shorten Monte Carlo derivatives pricing model development time and create models with faster execution speeds. SciFinance® now features support for NVIDIA® Tesla™ 20-series GPUs and CUDA™ 3.0.

“The mathematical problems of pricing derivatives are tailor-made for GPU computing, and Monte Carlo simulations enjoy some of the fastest speed-ups on GPUs: from 50 to over 300 times faster compared to serial code,” said Curt Randall, executive vice president of SciComp. “This execution speed increase makes it feasible to replace grid solutions (CPUs and interconnects) with a GPU system. GPU costs are a tiny percentage of the cost of a grid solution and offer radical reductions in both footprint and power consumption.”

SciFinance takes advantage of new GPU hardware and software from NVIDIA Read the rest of this entry »

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

ACUSim vortex shedding

From a recent press release:

ACUSIM Software, Inc., a leader in computational fluid dynamics (CFD) technology and solutions, today announced the immediate availability of AcuSolve™ 1.8, the latest version of ACUSIM’s leading general-purpose, finite-element based CFD solver. ACUSIM will demonstrate AcuSolve 1.8 during two free webinars, taking place at 9:30 a.m. – 10:30 a.m. ET and 6:30 p.m. – 7:30 p.m. ET, on Oct. 26, 2010, at http://www.acusim.com/html/events.html.

Used by designers and research engineers with all levels of expertise, AcuSolve is highly differentiated by its accelerated speed, robustness, accuracy and multiphysics/multidisciplinary capabilities. Contributing to its robustness is the product’s Galerkin/Least-Square (GLS) finite element formulation and novel iterative linear equation solver for the fully coupled equation system. The combination of these two powerful technologies provides a highly stable and efficient solver, capable of handling unstructured meshes with tight boundary layers automatically generated from complex industrial geometries. Read the rest of this entry »

IMPETUS Afea is proud to announce the launch of IMPETUS Afea Solver (version 1.0).

The IMPETUS Afea Solver is a non-linear explicit finite element tool. It is developed to predict large deformations of structures and components exposed to extreme loading conditions. The tool is applicable to transient dynamics and quasi-static loading conditions. The primary focus of the IMPETUS Afea Solver is accuracy, robustness and simplicity for the user. The number of purely numerical parameters that the user has to provide as input is kept at a minimum. The IMPETUS Afea Solver is adapted to GPU technology; utilizing the computational force of a potent graphics card can considerably speed up your calculations.

IMPETUS Afea Solver Video on YouTube

For more information or requests please contact sales@impetus-afea.com

NVIDIA today announced the release of NVIDIA Parallel Nsight software, the industry’s first development environment for GPU-accelerated applications that work with Microsoft Visual Studio.  ”By adding functionality specifically for GPU Computing developers, Parallel Nsight makes the power of the GPU more accessible than ever before,” said Sanford Russell, GM of GPU Computing at NVIDIA. NVIDIA Parallel NSight features a CUDA C/C++ debugger and application performance analyzer, and a graphics debugger and inspector.  NVIDIA Parallel Nsight supports Windows HPC Server 2008, Windows 7 and Windows Vista.  Download Parallel Nsight here.

EM Photonics announced today the general availability of CULA 2.0, its GPU-accelerated linear algebra library. The new version provides support for NVIDIA GPUs based on the latest “Fermi” architecture.

CULA contains a LAPACK interface comprised of over 150 mathematical routines from the industry standard for computational linear algebra, LAPACK. EM Photonics’ CULA library includes many popular routines including system solvers, least squares solvers, orthogonal factorizations, eigenvalue routines, and singular value decompositions. CULA offers performance up to a magnitude faster than highly optimized CPU-based linear algebra solvers. There is a variety of different interfaces available to integrate directly into your existing code. Programmers can easily call GPU-accelerated CULA from their C/C++, FORTRAN, MATLAB, or Python codes. This can all be done with no GPU programming experience. CULA is available for every system equipped with GPUs based on the NVIDIA CUDA architecture. This includes 32- and 64-bit versions of Linux, Windows, and OS X.

More information is available at www.culatools.com.

Graphic Remedy is proud to announce the release of gDEBugger Version 5.6 for Windows, Linux, Mac OS X, iPhone and iPad. This version introduces iPhone and iPad on-device debugging and profiling abilities, letting developers optimize their apps in real-time on actual iPhone and iPad hardware, while viewing invaluable inside information such as the device’s GPU, CPU, graphics driver and operating system performance counters.

gDEBugger is an OpenGL, OpenGL ES and OpenCL debugger and profiler that traces application activity on top of the OpenGL API, and lets programmers see what is happening within the graphics system implementation to find bugs and optimize OpenGL application performance. gDEBugger runs on Windows, Mac OS X, iPhone and Linux operating systems.

SagivTech plans to offer a 3-days course that deals with Image Processing with CUDA in the USA this September. This is an advanced course that is intended for experienced CUDA developers looking for optimization methods for image processing applications implemented on NVIDIA GPUs.

The course will be held in the San Francisco area, 9am to 5pm September 27-29.

Read the rest of this entry »

GE has introduced three new rugged computing products featuring integrated GPGPU technology using NVIDIA CUDA-capable GPUs.  The first is the IPN250 Rugged 6U OpenVPX Single Board Computer (SBC).  The second is the 6U OpenVPX NPN240 multi-processor. The NPN240 features two NVIDIA® CUDA-capable GT240 96-core GPUs, enabling it to deliver up to 750 GFLOP/S peak per card slot (depending on the application). Multiple NPN240s can be linked to one or more hosts to create multi-node CUDA GPU clusters capable of thousands of GFLOP/S.  The third is the OpenVPX-compatible GRA111 high performance graphics board, which is the first rugged implementation of a CUDA-capable GPU.