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cs.DC2020
Matrix Engines for High Performance Computing:A Paragon of Performance or Grasping at Straws?
Jens Domke, Emil Vatai, Aleksandr Drozd +8
Matrix engines or units, in different forms and affinities, are becoming a reality in modern processors; CPUs and otherwise. The current and dominant algorithmic approach to Deep L…
cs.DC2020★ 9 cited
The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism
Yosuke Oyama, Naoya Maruyama, Nikoli Dryden +6
We present scalable hybrid-parallel algorithms for training large-scale 3D convolutional neural networks. Deep learning-based emerging scientific workflows often require model trai…