![[article image]](https://cacm.acm.org/system/assets/0002/9360/111717_Andrej_Karpathy_images.large.jpg?1510940163&1510940162 "Two-dimensional embedding of images from the ImageNet database, extracted by a convolutional neural network using Caffe.")
A team led by researchers at the Texas Advanced Computing Center (TACC) recently published the results of an effort to use supercomputers to train a deep neural network for rapid image recognition.
They used the Stampede2 supercomputer to complete a 100-epoch ImageNet training with AlexNet in 11 minutes, marking the fastest time recorded to date.
The team also completed a 90-epoch ImageNet training with ResNet-50 in 32 minutes.
"These results show the potential of using advanced computing resources...along with large mini-batch enabling algorithms, to train deep neural networks interactively and in a distributed way," says TACC's Zhao Zhang.
The research involved developing a Layer-Wise Adaptive Rate Scaling algorithm that distributes data efficiently to many processors to compute simultaneously using a batch size of up to 32,000 items.
"By not having to migrate large datasets between specialized hardware systems, the time to data-driven discovery is reduced and overall efficiency can be significantly increased," says TACC's Niall Gaffney.
From Texas Advanced Computing Center
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