Graphic Processor Units for Many-particle Dynamics


A few days ago a friend came to me with a question about floating point.  Let me start by saying that my friend knows his stuff, he doesn't ask stupid questions.  So he had my attention.  He was working on some biosciences simulation code and was getting answers of a different precision than he expected on the GPU  and wanted to know what was up.


World's Top Design & Manufacturing Companies Cut Engineering Simulation Times in Half Using GPU-Accelerated Abaqus FEA

 Two of the largest automakers in Europe evaluated Abaqus FEA to analyze the structural behavior of large engine models. In each case their NVIDIA GPU-accelerated simulations were completed in half the time it would have taken using CPUs alone. For the automotive industry, this acceleration enables CAE engineers to perform more analyses in a given period of time, identifying issues earlier and decreasing time to market.


Atlanta, GA, Jun 1, 2011 - AccelerEyes today released version 1.0 of libJacket enabling GPU programmers to achieve better GPU performance with less programming hassle. It builds upon the big success of Jacket for MATLAB®, bringing that popular runtime system and vast GPU function library to new programming languages.

Whether racing to model fast-moving financial markets, exploring mountains of geological data, or researching solutions to complex scientific problems, you need a computing platform that delivers the highest throughput and lowest latency possible. GPU-accelerated clusters and workstations are widely recognized for providing the tremendous horsepower required to perform compute-intensive workloads, and your applications can achieve even faster results with NVIDIA GPUDirect™.

  Well NVIDIA waited exactly five days into the new year to announce a major new direction for its product roadmap. On Wednesday, the GPU-maker -- and soon to be CPU-maker -- revealed its plans to build heterogeneous processors, which will encompass high performance ARM CPU cores alongside GPU cores.

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NVIDIA is calling on global researchers to submit their innovations for the NVIDIA Global Impact Award - an annual grant of $150,000 for groundbreaking work that addresses the world's most important social and humanitarian problems. 

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Unified Memory in CUDA 6

With CUDA 6, we’re introducing one of the most dramatic programming model improvements in the history of the CUDA platform, Unified Memory. In a typical PC or cluster node today, the memories of the CPU and GPU are physically distinct and separated by the PCI-Express bus. Before CUDA 6, that is exactly how the programmer has to view things. Data that is shared between the CPU and GPU must be allocated in both memories, and explicitly copied between them by the program. This adds a lot of complexity to CUDA programs.

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