Скачать или смотреть Extreme Scale Deep Learning at NERSC

In this video from PASC18, Thorsten Kurth from Lawrence Berkeley National Laboratory presents: Extreme Scale Deep Learning at NERSC.

"We present various studies on very large scale distributed deep learning on HPC systems including the ~10k node Intel Xeon-Phi-based Cori system at NERSC. We explore CNN classification
architectures and generative adversarial networks for HEP problems using large images corresponding to full LHC detectors and high-resolution cosmology convergence maps. We have
explored distributed scaling in different deep-learning frameworks, including Caffe, TensorFlow and PyTorch with different communication layers, i.e. Google RPC or MPI-based approaches
such as Intel MLSL, Uber Horovod and Cray’s CPE ML Plugin. We describe various approaches for scaling out the training of single models up to the full Cori system. We further discuss recent
work contrasting performance with different frameworks, systems and system architectures.

Co-Author(s): Wahid Bhimji (Lawrence Berkeley National Laboratory, USA)

Learn more: https://pasc18.pasc-conference.org/

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