Date of Original Version
Open access to the Proceedings of the 11th USENIX Symposium on Operating Systems Design and Implementation is sponsored by USENIX
Abstract or Description
We propose a parameter server framework for distributed machine learning problems. Both data and workloads are distributed over worker nodes, while the server nodes maintain globally shared parameters, represented as dense or sparse vectors and matrices. The framework manages asynchronous data communication between nodes, and supports flexible consistency models, elastic scalability, and continuous fault tolerance.
To demonstrate the scalability of the proposed framework, we show experimental results on petabytes of real data with billions of examples and parameters on problems ranging from Sparse Logistic Regression to Latent Dirichlet Allocation and Distributed Sketching.
Proceedings of the USENIX Symposium on Operating Systems Design and Implementation, 2014, 583-598.