Consistency for Fast Data-Parallel Iterative Analytics

At the core of Machine Learning (ML) analytics applied to Big Data is often an expert-suggested model, whose parameters are refined by iteratively processing a training dataset until convergence. The completion time (i.e. convergence time) and quality of the learned model not only depends on the rate at which the refinements are generated but also the quality of each refinement. While data-parallel ML applications often employ a loose consistency model when updating shared model parameters to maximize parallelism, the accumulated error may seriously impact the quality of refinements and thus delay completion time, a problem that usually gets worse with scale. Although more immediate propagation of updates reduces the accumulated error, this strategy is limited by physical network bandwidth. Additionally, the performance of the widely used stochastic gradient descent (SGD) algorithm is sensitive to initial step size, simply increasing communication without adjusting the step size value accordingly fails to achieve optimal performance.