Search Results for author: Garth A. Gibson

Found 5 papers, 0 papers with code

On Model Parallelization and Scheduling Strategies for Distributed Machine Learning

no code implementations NeurIPS 2014 Seunghak Lee, Jin Kyu Kim, Xun Zheng, Qirong Ho, Garth A. Gibson, Eric P. Xing

Distributed machine learning has typically been approached from a data parallel perspective, where big data are partitioned to multiple workers and an algorithm is executed concurrently over different data subsets under various synchronization schemes to ensure speed-up and/or correctness.

Primitives for Dynamic Big Model Parallelism

no code implementations18 Jun 2014 Seunghak Lee, Jin Kyu Kim, Xun Zheng, Qirong Ho, Garth A. Gibson, Eric P. Xing

When training large machine learning models with many variables or parameters, a single machine is often inadequate since the model may be too large to fit in memory, while training can take a long time even with stochastic updates.

Structure-Aware Dynamic Scheduler for Parallel Machine Learning

no code implementations19 Dec 2013 Seunghak Lee, Jin Kyu Kim, Qirong Ho, Garth A. Gibson, Eric P. Xing

Training large machine learning (ML) models with many variables or parameters can take a long time if one employs sequential procedures even with stochastic updates.

Distributed Computing

More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server

no code implementations NeurIPS 2013 Qirong Ho, James Cipar, Henggang Cui, Seunghak Lee, Jin Kyu Kim, Phillip B. Gibbons, Garth A. Gibson, Greg Ganger, Eric P. Xing

We propose a parameter server system for distributed ML, which follows a Stale Synchronous Parallel (SSP) model of computation that maximizes the time computational workers spend doing useful work on ML algorithms, while still providing correctness guarantees.

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