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High End Computing

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  • Deep learning is all about doing large searches and operations in the vector space. CUDA parallel programming is the main concept that allows you to divide any computation work over a large vectors per row, so you can distribute it more efficiently across different cores.
  • Nvidia has the dominance of Deep Learning because of the hegemony of CUDA, a proprietary programming framework. Because Nvidia was the first one to come up with a solution needed by data scientists to process large amounts of data.
  • NVIDIA dominates deep learning because Tensorflow and Pythorch both are implemented on top of cuDNN, also developed by NVIDIA and is built on top of CUDA, is a library of GPU-accelerated primitives for deep neural networks.
  • The main reason that you need an NVIDIA GPU is because of CUDA. CUDA is a proprietary programming framework developed by NVIDIA that facilitates massive parallelization of computing tasks using the cores in an NVIDIA GPU.
  • Actually, CUDA cores are not really the same as CPU cores. A CPU core is capable of fetching instructions, doing computations, replacing values in memory, releasing memory, writing to disk. A CUDA core on the other hand is only capable of processing computations in a single row of a vector. Much more limited than what a CPU core can do. But that doesn’t make it any less important to Deep Learning.
  • When picking the best CPU for Deep Learning, it is better to have a higher number of cores rather than having a high clock speed. The idea is that the higher number of parallel jobs you can start, the better it is for Deep Learning.