9 citations · 9 across the 3 of their papers we have counts for
3 papers
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…
cs.LG2018
μ-cuDNN: Accelerating Deep Learning Frameworks with Micro-Batching
Yosuke Oyama, Tal Ben-Nun, Torsten Hoefler +1
NVIDIA cuDNN is a low-level library that provides GPU kernels frequently used in deep learning. Specifically, cuDNN implements several equivalent convolution algorithms, whose perf…