Big Learning (Systems for ML)

Contact: Greg Ganger, Phil Gibbons, Garth Gibson, Eric Xing, Jinliang Wei

Data analytics (a.k.a. Big Data) has emerged as a primary data processing activity for business, science, and online services that attempt to extract insight from quantities of observation data. Increasingly, such analytics center on statistical machine learning (ML), in which an algorithm determines model parameters that make a chosen statistical model best fit the training data. Once fit (trained), such models can expose relationships among data items (e.g., for grouping documents into topics), predict outcomes for new data items based on selected characteristics (e.g., for recommendation systems), correlate effects with causes (e.g., for genomic analyses of diseases), and so on.

Growth in data sizes and desired model precision generally dictates parallel execution of ML algorithms on clusters of servers. Naturally, parallel ML involves the same work distribution, synchronization and data consistency challenges as other parallel computations. The PDL big-learning group has attacked these challenges, creating and refining powerful new approaches for supporting large-scale ML on Big Data. This short article overviews an inter-related collection of our efforts in this space. Click here for a short article overviewing an inter-related collection of our efforts in this space.



Greg Ganger
Phil Gibbons
Garth Gibson
Eric Xing


Jin Kyu Kim
Henggang Cui
Wei Dai
Jinliang Wei
Aaron Harlap


Qirong Ho
James Cipar


CMU Sailing Lab




We thank the members and companies of the PDL Consortium: Broadcom, Ltd., Citadel, Dell EMC, Google, Hewlett-Packard Labs, Hitachi Ltd., Intel Corporation, Microsoft Research, MongoDB, NetApp, Inc., Oracle Corporation, Samsung Information Systems America, Seagate Technology, Tintri, Two Sigma, Uber, Veritas and Western Digital for their interest, insights, feedback, and support.




© 2017. Last updated 7 February, 2017