Intel has acquired RapidMind, the company behind the RapidMind (formerly Sh) programming environment targeting multicore CPUs, AMD and NVIDIA GPUs and the Cell processor. The RapidMind Platform continues to be available, including support. In the medium term RapidMind’s technology and products will be integrated with Intel’s data-parallel products, in particular Intel’s Ct technology.
This blog entry by James Reinders from Intel describes the acquisition and future plans in more detail.
EM Photonics has recently released a preview beta edition of their CULAtools, an implementation of LAPACK for CUDA-enabled GPUs. This version comprises single precision LU decomposition, QR factorization, singular value decomposition and least squares. The full library, scheduled for release at NVIDIA GTC ’09, will contain much more functionality and in particular single- and double-precision computations. Please refer to the website culatools.com for details, licenses and downloads.
Penguin Computing has launched a new service that enables high-performance computing within a cloud-computing infrastructure, including support for GPU computing with NVIDIA Tesla GPUs. From HPCWire:
SAN FRANCISCO, Aug. 11 — Penguin Computing, experts in high performance computing solutions, today announced the immediate availability of “Penguin on Demand” — or POD — a new service that delivers, for the first time, a complete high performance computing (HPC) solution in the cloud. POD extends the concept of cloud computing by making optimized compute resources designed specifically for HPC available on demand. POD is targeted at researchers, scientists and engineers who require surge capacity for time-critical analyses or organizations that need HPC capabilities without the expense and effort required to acquire HPC clusters.
POD provides a computing infrastructure of highly optimized Linux clusters with specialized hardware interconnects and software configurations tuned specifically for HPC. Rather than utilizing machine virtualization, as is typical in traditional cloud computing, POD allows users to access a server’s full resources at one time for maximum performance and I/O for massive HPC workloads.
Comprising high-density Xeon-based compute nodes coupled with high-speed storage, POD provides a persistent compute environment that runs on a head node and executes directly on the compute nodes’ physical cores. Both GigE and DDR high-performance Infiniband network fabrics are available. POD customers also get access to state-of-the-art GPU supercomputing with NVIDIA Tesla processor technology. Jobs typically run over a localized network topology to maximize inter-process communication, to maximize bandwidth and minimize latency.
Equalizer 0.9, a framework for creating and deploying parallel, scalable OpenGL applications, has been released. The most notable new features in this release are:
See the 0.9 release notes on the Equalizer website for a comprehensive list of new features, enhancements, optimizations and bug. A paperback Equalizer Programming and User Guide is available from Lulu.com. Commercial support, custom software development and porting services are available from Eyescale Software GmbH.
LabVIEW GPU Computing unleashes the computing power of NVIDIA GPUs via the CUDA interface from within a LabVIEW application. Code that calls the GPU for computation is integrated into the native parallel execution system of LabVIEW as if it were any other multi-threaded external library function call.
LabVIEW is a graphical programming environment used by millions of engineers and scientists to develop sophisticated measurement, test, and control systems using intuitive graphical icons and wires that resemble a flowchart. LabVIEW offers unrivaled integration with thousands of hardware devices and provides hundreds of built-in libraries for advanced analysis and data visualization. The LabVIEW platform is scalable across multiple targets and operating systems, and since its introduction in 1986 has become an industry leader.
The GPU Technology Conference will be held Sept 30-Oct 2, 2009 in San Jose, Calif. This event will focus on the latest breakthroughs that developers, engineers and researchers are achieving through the use of the GPU. Learn more at www.nvidia.com/gtc
Session abstracts and speakers can be found at www.nvidia.com/gtc under the Agenda page. Sessions announced to date include
More sessions are to be announced.
The course notes and supplementary material for “Beyond Programmable Shading”, a full-day course held at SIGGRAPH 2009 on August 6, are now available online.
This course is presented in two parts, Beyond Programmable Shading I and Beyond Programmable Shading II.
There are strong indications that the future of interactive graphics programming is a more flexible model than today’s OpenGL/Direct3D pipelines. Graphics developers need a basic understanding of how to combine emerging parallel programming techniques and more flexible graphics processors with the traditional interactive rendering pipeline. The first half of the course introduces the trends and directions in this emerging field. Topics include: parallel graphics architectures, parallel programming models for graphics, and game-developer investigations of the use of these new capabilities in future rendering engines.
The second half of the course has leaders from graphics hardware vendors, game development, and academic research present case studies that show how general parallel computation is being combined with the traditional graphics pipeline to boost image quality and spur new graphics algorithm innovation. Each case study discusses the mix of parallel programming constructs used, details of the graphics algorithm, and how the rendering pipeline and computation interact to achieve the technical goals. Read the rest of this entry »
The MAGMA project aims to develop a dense linear algebra library similar to LAPACK but for heterogeneous/hybrid architectures, starting with current “Multicore+GPU” systems.
The MAGMA research is based on the idea that, to address the complex challenges of the emerging hybrid environments, optimal software solutions will themselves have to hybridized, combining the strengths of different algorithms within a single framework. Building on this idea, the MAGMA group aims to design linear algebra algorithms and frameworks for hybrid manycore and GPU systems that can enable applications to fully exploit the power that each of the hybrid components offers.
MAGMA v0.1 runs on CUDA-capable GPUs and multicore CPUs, and is available now.
Brook+, AMD’s extension of the BrookGPU programming environment, has been released in full source code to SourceForge. Brook+ supports an ATI CAL and x86 CPU backend, and allows developers to program GPUs in a C-like stream computing language.
AMD is now offering a free OpenCL for CPU beta download as part of the ATI Stream SDK v2.0 Beta Program. The beta will help programmers to more easily develop parallel software programs and take further advantage of multi-core x86 CPUs to accelerate software and deliver a better computing experience. AMD has submitted conformance logs from its Microsoft Windows and Linux CPU beta releases to the Khronos Working Group for certification.
The full press release is available here, and the SDK can be downloaded here.
GPGPU.org relies on news submissions from readers like you.
Have news? Submit It!
GPGPU Website Copyright © 2002-2012 GPGPU.org. All rights reserved.
Twitter feed