  We propose a new data-centric synchronization framework for carrying out of
machine learning (ML) tasks in a distributed environment. Our framework
exploits the iterative nature of ML algorithms and relaxes the application
agnostic bulk synchronization parallel (BSP) paradigm that has previously been
used for distributed machine learning. Data-centric synchronization complements
function-centric synchronization based on using stale updates to increase the
throughput of distributed ML computations. Experiments to validate our
framework suggest that we can attain substantial improvement over BSP while
guaranteeing sequential correctness of ML tasks.
