Add to ORKGWe propose a new integrated method of exploiting model, batch and domain parallelism for the training of deep neural networks (DNNs) on large distributed-memory computers using minibatch stochastic gradient descent (SGD). Our goal is to find an efficient parallelization strategy for a fixed batch size using P processes. Our method is inspired by the communication-avoiding algorithms in numerical linear algebra. We see P processes as logically divided into a Pr × Pc grid where the Pr dimension is implicitly responsible for model/domain parallelism and the Pc dimension is implicitly responsible for batch parallelism. In practice, the integrated matrix-based parallel algorithm encapsulates these types of parallelism automatically. We analyze t...
Neural Networks (NNs) are getting deeper and more complicated to the point where single accelerator ...
Parallelizing neural networks is an active area of research. Current approaches surround the paralle...
Parallelizing neural networks is an active area of research. Current approaches surround the paralle...
We propose a new integrated method of exploiting model, batch and domain parallelism for the trainin...
We propose a new integrated method of exploiting model, batch and domain parallelism for the trainin...
Deep neural network models can achieve greater performance in numerous machine learning tasks by rai...
In distributed training of deep neural networks, parallel minibatch SGD is widely used to speed up t...
We describe the neural-network training framework used in the Kaldi speech recogni-tion toolkit, whi...
We describe the neural-network training framework used in the Kaldi speech recogni-tion toolkit, whi...
We describe the neural-network training framework used in the Kaldi speech recogni-tion toolkit, whi...
This paper proposes an efficient asynchronous stochastic second order learning algorithm for distrib...
Deep neural networks (DNN) have recently achieved extraordinary results in domains like computer vis...
Deep neural networks (DNN) have recently achieved extraordinary results in domains like computer vis...
Mini-batch stochastic gradient methods (SGD) are state of the art for distributed training of deep n...
Accelerating and scaling the training of deep neural networks (DNNs) is critical to keep up with gro...
Neural Networks (NNs) are getting deeper and more complicated to the point where single accelerator ...
Parallelizing neural networks is an active area of research. Current approaches surround the paralle...
Parallelizing neural networks is an active area of research. Current approaches surround the paralle...
We propose a new integrated method of exploiting model, batch and domain parallelism for the trainin...
We propose a new integrated method of exploiting model, batch and domain parallelism for the trainin...
Deep neural network models can achieve greater performance in numerous machine learning tasks by rai...
In distributed training of deep neural networks, parallel minibatch SGD is widely used to speed up t...
We describe the neural-network training framework used in the Kaldi speech recogni-tion toolkit, whi...
We describe the neural-network training framework used in the Kaldi speech recogni-tion toolkit, whi...
We describe the neural-network training framework used in the Kaldi speech recogni-tion toolkit, whi...
This paper proposes an efficient asynchronous stochastic second order learning algorithm for distrib...
Deep neural networks (DNN) have recently achieved extraordinary results in domains like computer vis...
Deep neural networks (DNN) have recently achieved extraordinary results in domains like computer vis...
Mini-batch stochastic gradient methods (SGD) are state of the art for distributed training of deep n...
Accelerating and scaling the training of deep neural networks (DNNs) is critical to keep up with gro...
Neural Networks (NNs) are getting deeper and more complicated to the point where single accelerator ...
Parallelizing neural networks is an active area of research. Current approaches surround the paralle...
Parallelizing neural networks is an active area of research. Current approaches surround the paralle...