We are witnessing the move towards data center operating systems (OS), where resources are unified and processing frameworks coexist with each other. In this context it has been shown that an iteration model with relaxed consistency such as the Stale Synchronous Parallel (SSP) model, while still guaranteeing convergence, is able to cope with the straggler problem for converging iterative algorithms. In this poster we present a model for the integration of the SSP model on a pipelined processing framework. We then apply the SSP on a distributed version of the Frank-Wolfe algorithm and empirically show its convergence under stress situations similar to those encountered in a data center OS.
Thomas Peel, and Nam-Luc Tran, Distributed Frank-Wolfe under Pipelined Stale Synchronous Parallelism, poster at the Greed is Great ICML’15 Workshop, Lille, France, July 2015