Article 52ZT4 How to Achieve High-Performance, Scalable and Distributed DNN Training on Modern HPC Systems

How to Achieve High-Performance, Scalable and Distributed DNN Training on Modern HPC Systems

by
Rich Brueckner
from High-Performance Computing News Analysis | insideHPC on (#52ZT4)
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DK Panda from Ohio State University gave this talk at the Stanford HPC Conference. "This talk will focus on a range of solutions being carried out in my group to address these challenges. The solutions will include: 1) MPI-driven Deep Learning, 2) Co-designing Deep Learning Stacks with High-Performance MPI, 3) Out-of- core DNN training, and 4) Hybrid (Data and Model) parallelism. Case studies to accelerate DNN training with popular frameworks like TensorFlow, PyTorch, MXNet and Caffe on modern HPC systems will be presented."

The post How to Achieve High-Performance, Scalable and Distributed DNN Training on Modern HPC Systems appeared first on insideHPC.

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