Today NVIDIA today unveiled a new family of Tesla® GPUs based on the revolutionary NVIDIA® Kepler™ GPU computing architecture, which makes GPU-accelerated computing easier and more accessible for a broader range of high performance computing (HPC) scientific and technical applications. The new NVIDIA Tesla K10 and K20 GPUs are computing accelerators built to handle the…
The OpenACC standards group today announced growing support for OpenACC-supported development tools, and initial resultsfrom programmers who have been using the recently-released OpenACC compilers to accelerate research.
NVIDIA today announced that LLVM, one of the industry’s most popular open source compilers, now supports NVIDIA GPUs, dramatically expanding the range of researchers, independent software vendors (ISVs) and programming languages that can take advantage of the benefits of GPU acceleration.
Do not miss the opening keynote, featuring Jen-Hsun Huang, CEO and Co-Founder of NVIDIA. Hear about what’s next in computing and graphics, and preview disruptive technologies and exciting demonstrations from across industries. Jen-Hsun co-founded NVIDIA in 1993 and has served since its inception as president, chief executive officer and a member of the board of…
This is a special Meetup co-located with the GPU Technology Conference! Exchange ideas, meet experts, share code… all HPC & GPU, all practical, all cutting-edge.
NCSA will host two courses offered by the Virtual School of Computational Science and Engineering (VSCSE) this summer: Programming Heterogeneous Parallel Computing Systems and…
The greatest minds in the scientific, engineering, research and development communities from over 40 different countries will gather to discuss how GPUs are enabling breakthroughs across a range of scientific and commercial fields.
A special track has been added on the Rapid Development of Accelerators for the Biological Sciences. To design accelerators that have a significant impact on the biosciences, the computer science community must revolutionize the way accelerated applications and their underlying hardware are developed and deployed.
The National Center for Supercomputing Applications’ Blue Waters and the Extreme Science and Engineering Discovery Environment (XSEDE) projects are hosting the annual Extreme Scaling Workshop on July 15-16, 2012, in Chicago.
OpenCV have made it easier for application developers to use computer vision. They are well-documented and vibrant open source projects that keep growing, and they are being adapted to new computing technologies
In order to improve the efficiency of the algorithm, we implemented the reconstruction algorithm on GPU in parallel way and tested the program on several large scale data sets.
Integrated spatio-temporal assessment and modelling of complex social–ecological systems is required to address global environmental challenges.
The CUDA 5 Preview release is now available to you via the CUDA Registered Developer program. CUDA 5 introduces several new tools and features that make it easier than ever to add GPU acceleration to your applications.
NVIDIA today announced that LLVM, one of the industry’s most popular open source compilers, now supports NVIDIA GPUs, dramatically expanding the range of researchers, independent software vendors (ISVs) and programming languages that can take advantage of the benefits of GPU acceleration.
Learn how the Intel® SDK for OpenCL* Applications 2012 now supports 3rd generation Intel® Core™ processors on both Intel® Processors and Intel® HD graphics 4000/2500. Join Suresh Rangarajulu, Marketing Manager for Intel® HD Graphics as he explains.
What makes debugging of multiprocess and multithread applications so difficult? The first thing that comes to mind of every concurrent programmer is the lack of program execution reproducibility. The reason for such program behavior is the preemptive scheduling employed by real-time operating systems.
The paper presents the Xcelerit SDK, a parallel programming toolkit that leverages the dataflow programming model to efficiently use multi-core CPUs, graphics processors (GPUs), and combinations of these in a cluster (or grid) from a single high-level source code.
This is “programmer’s introduction” where we cover the ideas behind OpenCL but also show how these ideas are translated into source code. We will do this through a series of progressively more challenging examples
GPUs accelerate functional neuroimaging, by using three GPUs, 850 TB of data can be analyzed in 10 days, compared to 100 years with conventional software!
In this paper, we analyze Smoldyn, a widely diffused algorithm for stochastic simulation of chemical reactions with spatial resolution and single molecule detail, and we propose an alternative, innovative implementation that exploits the parallelism of GPU
We introduce a new hardware architecture, based on which the finite difference wavefield-continuation depth migration can be conducted using the GPU as a CPU coprocessor.
In this thesis, a novel three-dimensional anisotropic front propagation algorithm for simulation of geological folds on parallel architecture is presented. The algorithm’s abundant parallelism is demonstrated on multi-core CPUs and GPU architectures.
We present the first MPI-CUDA implementation of Finite-Difference Time-Domain (FDTD) discretization of Maxwell’s equations in dispersive media that uses the MPI API to assign each CPU node its share of the computational domain and GPUs to their corresponding CPU threads
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