Data parallelism is a common way to parallelize stochastic gradient descent (SGD). However, the loss of convergence at large minibatch sizes limits the scalability of data parallelism. This paper introduces a novel method to combine gradients called Adasum that significantly improves the convergence when using large minibatches. This paper provides the intuition and formal justification of Adasum along with a convergence proof. Additionally, the paper describes an efficient implementation of Ada…