A Weighted Aggregating SGD for Scalable Parallelization in Deep Learning We investigate the stochastic optimization problem and develop a scalable parallel computing algorithm for deep learning tasks. The key of our study involves a reformation of the objective function for the stochastic optimization in neural network models. We propose a novel update rule, named weighted aggregating stochastic gradient decent, after theoretically analyzing the characteristics of the newly formalized objective function. The new rule introduces a weighted aggregation scheme based on the performance of local workers and does not require a center variable. It assesses the relative importance of local workers and accepts them according to their contributions. Our new rule also allows the implementation of both synchronous and asynchronous parallelization and can result in varying convergence rates. For method evaluation, we benchmark our schemes against the mainstream algorithms, including the elastic averaging SGD in training deep neural networks for classification tasks. We conduct extensive experiments on several classic datasets, and the results confirm the strength of our scheme in accelerating the training of deep architecture and scalable parallelization.

Taming Unbalanced Training Workloads in Deep Learning with Partial Collective Operations Load imbalance pervasively exists in distributed deep learning training systems, either caused by the inherent imbalance in learned tasks or by the system itself. Traditional synchronous Stochastic Gradient Descent (SGD) achieves good accuracy for a wide variety of tasks, but relies on global synchronization to accumulate the gradients at every training step. In this paper, we propose eagerSGD, which relaxes the global synchronization for decentralized accumulation. To implement eagerSGD, we propose to use two partial collectives: solo and majority. With solo allreduce, the faster processes contribute their gradients eagerly without waiting for the slower processes, whereas with majority allreduce, at least half of the participants must contribute gradients before continuing, all without using a central parameter server. We theoretically prove the convergence of the algorithms and describe the partial collectives in detail. Experimental results on loadimbalanced environments (CIFAR10, ImageNet, and UCF101 datasets) show that eagerSGD achieves 1.27x speedup over the stateoftheart synchronous SGD, without losing accuracy.NShigang LiDepartment of Computer Science, ETH ZurichTal BenNunDepartment of Computer Science, ETH ZurichSalvatore Di GirolamoDepartment of Com