Accelerated computing uses parallel processing to speed up work on demanding applications, from AI and data analytics to simulations and visualizations.
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Many of today��s applications process large volumes of data. While GPU architectures have very fast HBM or GDDR memory, they have limited capacity. Making the most of GPU performance requires the data to be as close to the GPU as possible. This is especially important for applications that iterate over the same data multiple times or have a high flops/byte ratio. Many real-world codes have to��
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One year ago today, NVIDIA announced the NVIDIA? DGX-1, an integrated system for deep learning. DGX-1 (shown in Figure 1) features eight Tesla P100 GPU accelerators connected through NVLink, the NVIDIA high-performance GPU interconnect, in a hybrid cube-mesh network. Together with dual socket Intel Xeon CPUs and four 100 Gb InfiniBand network interface cards, DGX-1 provides unprecedented��
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Modern computer architectures have a hierarchy of memories of varying size and performance. GPU architectures are approaching a terabyte per second memory bandwidth that, coupled with high-throughput computational cores, creates an ideal device for data-intensive tasks. However, everybody knows that fast memory is expensive. Modern applications striving to solve larger and larger problems can be��
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Artificial intelligence is already more ubiquitous than many people realize. Applications of AI abound, many of them powered by complex deep neural networks trained on massive data using GPUs. These applications understand when you talk to them; they can answer questions; and they can help you find information in ways you couldn��t before. Pinterest image search technology allows users to find��
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At the 2016 GPU Technology Conference in San Jose, NVIDIA CEO Jen-Hsun Huang announced the new NVIDIA Tesla P100, the most advanced accelerator ever built. Based on the new NVIDIA Pascal GP100 GPU and powered by ground-breaking technologies, Tesla P100 delivers the highest absolute performance for HPC, technical computing, deep learning, and many computationally intensive datacenter workloads.
]]>Today I��m excited to announce the general availability of CUDA 8, the latest update to NVIDIA��s powerful parallel computing platform and programming model. In this post I��ll give a quick overview of the major new features of CUDA 8. To learn more you can watch the recording of my talk from GTC 2016, ��CUDA 8 and Beyond��. A crucial goal for CUDA 8 is to provide support for the powerful new��
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For more recent info on NVLink, check out the post, ��How NVLink Will Enable Faster, Easier Multi-GPU Computing��. NVIDIA GPU accelerators have emerged in High-Performance Computing as an energy-efficient way to provide significant compute capability. The Green500 supercomputer list makes this clear: the top 10 supercomputers on the list feature NVIDIA GPUs. Today at the 2014 GPU Technology��
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