The Future of Data Science and Parallel Computing by Ganapathi Pulipaka

Author:Ganapathi Pulipaka [Pulipaka, Ganapathi]
Language: eng
Format: epub
Published: 2018-07-24T23:00:00+00:00

Facebook artificial intelligence group also developed a special open source library as part of Torch deep learning toolkit dubbed torch-rnnlib. This open source library allows developing newer recurrent network models and run those on GPUs for performance evaluation with minimum time allocated. The baselines can be attributed with cuDNN bindings. This is apart from the standard recurrent neural networks such as LTSM, RNN, and GRU. The benchmarks for such artificial intelligence framework processing colossal amounts of big data are measured through some standard benchmarks such as One Billion Word or EuroParl. These are complex training environments based on the large-scale size of the vocabularies that requires avoiding the overfitting and underfitting of the models through deep learning algorithms to perform a full-scale training with adaptive softmax on the GPUs. The benchmarks have shown that the processing capabilities are somewhat in the range of 12,500 words for each second a single GPU. This is a significant performance gain with high productivity levels of full softmax activation. This artificial intelligence framework and approach allowed the Facebook AI group to reduce the hardware infrastructure for big data processing through adaptive softmax approach with high accuracy.

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