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https://github.com/tmulc18/DistributedDeepLearningReads#distributed-deep-learning-reads
100-epoch ImageNet Training with AlexNet in 24 Minuteshttps://arxiv.org/abs/1709.05011
Accumulated Gradient Normalizationhttps://arxiv.org/abs/1710.02368
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hourhttps://arxiv.org/pdf/1706.02677.pdf
Asynchronous Parallel Stochastic Gradient for Nonconvex Optimizationhttp://papers.nips.cc/paper/5751-asynchronous-parallel-stochastic-gradient-for-nonconvex-optimization.pdf
Asynchrony begets Momentum, with an Application to Deep Learninghttps://arxiv.org/abs/1605.09774
Bandwidth Optimal All-reduce Algorithms for Clusters of Workstationshttp://www.cs.fsu.edu/~xyuan/paper/09jpdc.pdf
Bringing HPC Techniques to Deep Learninghttp://research.baidu.com/bringing-hpc-techniques-deep-learning/
Deep learning with Elastic Averaging SGDhttps://arxiv.org/abs/1412.6651
Distributed Delayed Stochastic Optimizationhttps://arxiv.org/abs/1104.5525
Don't Decay the Learning Rate, Increase the Batch Sizehttps://arxiv.org/abs/1711.00489
FireCaffe: near-linear acceleration of deep neural network training on compute clustershttps://arxiv.org/abs/1511.00175
Heterogeneity-aware Distributed Parameter Servershttps://ds3lab.org/wp-content/uploads/2017/07/sigmod2017_jiang.pdf
Hogwild!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descenthttps://people.eecs.berkeley.edu/~brecht/papers/hogwildTR.pdf
How to scale distributed deep learning?https://arxiv.org/abs/1611.04581
ImageNet Training in Minuteshttps://arxiv.org/abs/1709.05011
Joeri Hermans ADAG Bloghttp://joerihermans.com/ramblings/distributed-deep-learning-part-1-an-introduction/
Large Scale Distributed Deep Networkshttps://static.googleusercontent.com/media/research.google.com/en//archive/large_deep_networks_nips2012.pdf
Meet Horovod: Uber’s Open Source Distributed Deep Learning Framework for TensorFlowhttps://eng.uber.com/horovod/
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Serverhttp://repository.cmu.edu/cgi/viewcontent.cgi?article=1163&context=machine_learning
Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUshttps://arxiv.org/abs/1606.04487
On Parallelizability of Stochastic Gradient Descent for Speech DNNshttps://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ParallelSGD-ICASSP2014-published.pdf
On Scalable Deep Learning and Parallelizing Gradient Descenthttps://github.com/JoeriHermans/master-thesis/tree/master/thesis
One weird trick for parallelizing convolutional neural networkshttps://arxiv.org/abs/1404.5997
Parallel training of DNNs with Natural Gradient and Parameter Averaginghttps://arxiv.org/abs/1410.7455
Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machineshttps://arxiv.org/abs/1512.06216
PowerAI DDLhttps://arxiv.org/abs/1708.02188
Revisiting Distributed Synchronous SGDhttps://arxiv.org/pdf/1604.00981.pdf
Scalable Distributed DNN Training Using Commodity GPU Cloud Computinghttps://s3-us-west-2.amazonaws.com/amazon.jobs-public-documents/strom_interspeech2015.pdf
SparkNet: Training Deep Networks in Sparkhttps://arxiv.org/abs/1511.06051
Staleness-aware Async-SGD for Distributed Deep Learninghttps://arxiv.org/abs/1511.05950
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learninghttps://arxiv.org/abs/1705.07878
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