Optimizing multi-GPU parallelization strategies for deep learning training

S Pal, E Ebrahimi, A Zulfiqar, Y Fu, V Zhang… - Ieee …, 2019 - ieeexplore.ieee.org
Deploying deep learning (DL) models across multiple compute devices to train large and
complex models continues to grow in importance because of the demand for faster and …

Optimizing Multi-GPU Parallelization Strategies for Deep Learning Training

S Pal, E Ebrahimi, A Zulfiqar, Y Fu, V Zhang… - arXiv e …, 2019 - ui.adsabs.harvard.edu
Deploying deep learning (DL) models across multiple compute devices to train large and
complex models continues to grow in importance because of the demand for faster and …

Optimizing Multi-GPU Parallelization Strategies for Deep Learning Training

S Pal, E Ebrahimi, A Zulfiqar, Y Fu, V Zhang, S Migacz… - IEEE Micro, 2019 - computer.org
Deploying deep learning (DL) models across multiple compute devices to train large and
complex models continues to grow in importance because of the demand for faster and …

[PDF][PDF] Optimizing Multi-GPU Parallelization Strategies for Deep Learning Training

S Pal, E Ebrahimi, A Zulfiqar, Y Fu, V Zhang, S Migacz… - 2019 - nanocad.ee.ucla.edu
Deploying deep learning (DL) models across multiple compute devices to train large and
complex models continues to grow in importance because of the demand for faster and …

Optimizing Multi-GPU Parallelization Strategies for Deep Learning Training

S Pal, E Ebrahimi, A Zulfiqar, Y Fu, V Zhang… - arXiv preprint arXiv …, 2019 - arxiv.org
Deploying deep learning (DL) models across multiple compute devices to train large and
complex models continues to grow in importance because of the demand for faster and …

Optimizing Multi-GPU Parallelization Strategies for Deep Learning Training

S Pal, E Ebrahimi, A Zulfiqar, Y Fu, V Zhang, S Migacz… - IEEE Micro, 2019 - computer.org
Deploying deep learning (DL) models across multiple compute devices to train large and
complex models continues to grow in importance because of the demand for faster and …