Paper Info
Title
Snap ML: A Hierarchical Framework for Machine Learning
Abstract
We describe a new software framework for fast training of generalized linear models. The framework, named Snap Machine Learning (Snap ML), combines recent advances in machine learning systems and algorithms in a nested manner to reflect the hierarchical architecture of modern computing systems. We prove theoretically that such a hierarchical system can accelerate training in distributed environments where intra-node communication is cheaper than inter-node communication. Additionally, we provide a review of the implementation of Snap ML in terms of GPU acceleration, pipelining, communication patterns and software architecture, highlighting aspects that were critical for achieving high performance. We evaluate the performance of Snap ML in both single-node and multi-node environments, quantifying the benefit of the hierarchical scheme and the data streaming functionality, and comparing with other widely-used machine learning software frameworks. Finally, we present a logistic regression benchmark on the Criteo Terabyte Click Logs dataset and show that Snap ML achieves the same test loss an order of magnitude faster than any of the previously reported results, including those obtained using TensorFlow and scikit-learn.
Year
Venue
Keywords
2018
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018)
machine learning,the framework,software architecture,software framework,generalized linear models
Field
DocType
Volume
Hierarchical control system,Pipeline (computing),Terabyte,Snap,Artificial intelligence,Acceleration,Software architecture,Machine learning,Computing systems,Software framework,Mathematics
Conference
31
ISSN
Citations 
PageRank 
1049-5258
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Celestine Dunner1126.99
Thomas P. Parnell272.09
Dimitrios Sarigiannis301.01
Nikolas Ioannou4719.86
Andreea S. Anghel5367.74
Ravi Gummadi671.17
Kandasamy, Madhusudanan700.34
Haralampos Pozidis819327.36