Acceleware CUDA™ Training – Life Science Focus

May 2nd, 2012

Partnering with NVIDIA and Microsoft, this four day CUDA training course is designed for Researchers and Programmers in the life science industries who are looking to develop comprehensive skills in writing and optimizing applications that fully leverage the many-core processing capabilities of the GPU. It is held in Boston, MA, on June 4-7, 2012. This course will have a life science theme. Commonly used algorithms such as Monte Carlo methods, FFT and filtering will be used and profiled in examples. The case study on day 4 focuses on the efficient implementation of a molecular dynamics simulation. More information: http://www.acceleware.com/jun4boston

OpenCL SDK for new Intel Core Processors

April 27th, 2012

The Intel® SDK for OpenCL Applications now supports the OpenCL 1.1 full-profile on 3rd generation Intel® Core™ processors with Intel® HD Graphics 4000/2500. For the first time, OpenCL developers using Intel® architecture can utilize compute resources across both Intel® Processor and Intel HD Graphics. More information: http://software.intel.com/en-us/articles/vcsource-tools-opencl-sdk

New Libra Platform version released

April 21st, 2012

Libra Platform is a GPGPU-Heterogeneous Compute API and runtime environment available on Windows, Mac and Linux. Libra Compute API offers performance portability and direct compute access via standard programming environments C/C++, Java, C# and Matlab to execute math operations on top of current and future compute architectures, including the latest GPUs, x86/x64 CPUs and with broad support for compute devices compatible with low level specific APIs – OpenCL, CUDA, OpenGL and standard x86/x64 compute APIs.

Read more in the full announcement.

2 Day CUDA Workshop, May 5-6 2012, Berlin, Germany

April 21st, 2012

A 2 day CUDA workshop is taking place in Berlin, Germany on May 5 and 6 2012. Course details, outline and prices are available at http://cuda.eventbrite.com.

New rCUDA version beta testing

April 18th, 2012

The rCUDA Team is proud to announce a new version of the rCUDA framework which will include many new functionalities as well as boosted performance. This new version, cooked for over a year, will incorporate pipelined transfers, full multi-thread and multi-node capabilities, CUDA 4.1 support, global scheduler integration, support for CUDA C extensions, and native InfiniBand support. A closed beta teting program has been started. See the complete text at http://www.rcuda.net/index.php/news/19-new-revolutionary-version-of-rcuda-to-be-launched.html.

Scalable GPU graph traversal

April 17th, 2012

Abstract:

Breadth-first search (BFS) is a core primitive for graph traversal and a basis for many higher-level graph analysis algorithms. It is also representative of a class of parallel computations whose memory accesses and work distribution are both irregular and data-dependent. Recent work has demonstrated the plausibility of GPU sparse graph traversal, but has tended to focus on asymptotically inefficient algorithms that perform poorly on graphs with non-trivial diameter.

We present a BFS parallelization focused on fine-grained task management constructed from efficient prefix sum that achieves an asymptotically optimal O(|V|+|E|) work complexity. Our implementation delivers excellent performance on diverse graphs, achieving traversal rates in excess of 3.3 billion and 8.3 billion traversed edges per second using single and quad-GPU configurations, respectively. This level of performance is several times faster than state-of-the-art implementations both CPU and GPU platforms.

(Duane Merrill, Michael Garland and  Andrew Grimshaw: “Scalable GPU graph traversal”, Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming (PPoPP’12), pp.117-128, Feburary 2012. [DOI])

Acceleware 4 Day CUDA™ Course, Calgary

April 17th, 2012

Partnering with NVIDIA, this four day course (May 8-11, 2012) is designed for Programmers who are looking to develop comprehensive skills in writing and optimizing applications that fully leverage the multi-core processing capabilities of the GPU.

Delivered by Acceleware Developers, who provide real world experience and examples, the training comprises of classroom lectures and hands-on tutorials. Each student will be supplied with a laptop equipped with NVIDIA GPUs for the duration of the course. Small class sizes maximize learning and ensure a personal educational experience.

More information: http://www.acceleware.com/may8calgary

CFP: Deadline Extension – UKPEW 2012 – The 28th UK Performance Engineering Workshop

April 10th, 2012

UKPEW is the leading UK forum for the presentation of all aspects of performance modeling and analysis of computer and telecommunication systems. Original papers are invited on all relevant topics but papers on or related to the subjects listed below are particularly welcome.

The paper submission deadline has just been extended to April 20, 2012. The conference takes place June 2 and 3, 2012, in Edinburgh, UK. More Information: http://www.ukpew.org

Accelerate Your Science on the Titan Supercomputer

April 1st, 2012

Accelerate your science on the Titan Supercomputer later this year, by harnessing up to 20 petaflops of parallel processing using GPUs. Open to researchers from academia, government labs, and industry, the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program is the major means by which the scientific community gains access to some of the fastest supercomputers.

First, let INCITE know you are interested in GPU acceleration by completing a two-minute survey. Then determine if you want to submit a formal proposal by June 27, 2012.

Need help drafting your proposal? Attend a “how-to” webinar on Tuesday, April 24 to learn tips and tricks for drafting your proposal. For further questions about the call for proposals, please contact the INCITE manager at INCITE@DOEleadershipcomputing.org.

Adaptive Row-Grouped CSR Format For Storing of Sparse Matrices on GPU

April 1st, 2012

Abstract:

We present a new adaptive format for storing sparse matrices on GPU. We compare it with several other formats including CUSPARSE which is today probably the best choice for processing of sparse matrices on GPU in CUDA. Contrary to CUSPARSE which works with common CSR format, our new format requires conversion. However, multiplication of sparse-matrix and vector is significantly faster for many matrices. We demonstrate it on a set of 1600 matrices and we show for what types of matrices our format is profitable.

(Heller M., Oberhuber T., “Adaptive Row-Grouped CSR Format For Storing of Sparse Matrices on GPU“, preprint on Arxiv.org 2012, [PDF])

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