Deep learning has come to mean the most common implementation of a neural network for performing many AI tasks. Data scientists use software frameworks such as TensorFlow and PyTorch to develop and run DL algorithms. By this point, there has been a lot written about deep learning, and you can find more detailed information from many sources. For a good high-level summary, see What��s the��
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As AI and HPC datasets continue to increase in size, the time spent loading data for a given application begins to place a strain on the total application��s performance. When considering end-to-end application performance, fast GPUs are increasingly starved by slow I/O. I/O, the process of loading data from storage to GPUs for processing, has historically been controlled by the CPU.
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Accelerated systems have become the new standard for high performance computing (HPC) as GPUs continue to raise the bar for both performance and energy efficiency. In 2012, Oak Ridge National Laboratory announced what was to become the world��s fastest supercomputer, Titan, equipped with one NVIDIA? GPU per CPU �C over 18 thousand GPU accelerators. Titan established records not only in absolute��
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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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