FlexRR provides a scalable, efficient solution to the straggler problem for iterative machine learning (ML). The frequent (e.g., per iteration) barriers used in traditional BSP-based distributed ML implementations cause every transient slowdown of any worker thread to delay all others. FlexRR combines a more flexible synchronization model with dynamic peer-to-peer re-assignment of work among workers to address straggler threads. Experiments with real straggler behavior observed on Amazon EC2 and Microsoft Azure, as well as injected straggler behavior stress tests, confirm the significance of the problem and the effectiveness of FlexRR's solution. Using FlexRR, we consistently observe near-ideal run-times (relative to no performance jitter) across all real and injected straggler behaviors tested.
['Aaron Harlap', 'Henggang Cui', 'Wei Dai', 'Jinliang Wei', 'Gregory R. Ganger', 'Phillip B. Gibbons', 'Garth A. Gibson', 'Eric P. Xing']
Addressing the straggler problem for iterative convergent parallel ML