No
Yes
View More
View Less
Working...
Close
OK
Cancel
Confirm
System Message
Delete
Schedule
An unknown error has occurred and your request could not be completed. Please contact support.
Scheduled
Wait Listed
Personal Calendar
Speaking
Conference Event
Meeting
Interest
Schedule TBD
Conflict Found
This session is already scheduled at another time. Would you like to...
Loading...
Please enter a maximum of {0} characters.
Please enter a maximum of {0} words.
must be 50 characters or less.
must be 40 characters or less.
Session Summary
We were unable to load the map image.
This has not yet been assigned to a map.
Search Catalog
Reply
Replies ()
Search
New Post
Microblog
Microblog Thread
Post Reply
Post
Your session timed out.
This web page is not optimized for viewing on a mobile device. Visit this site in a desktop browser to access the full set of features.
2017 GTC San Jose

S7127 - cuMF_sgd: Fast and Scalable Matrix Factorization on GPUs

Session Speakers
Session Description

Matrix factorization (MF) has been widely used in recommender systems, topic modeling, word embedding, and more. Stochastic gradient descent (SGD) for MF is memory bound. Meanwhile, single-node CPU systems with caching performs well only for small datasets. Distributed systems have higher aggregated memory bandwidth but suffer from relatively slow network connections. This observation inspires us to accelerate MF by utilizing GPUs's high memory bandwidth and fast intra-node connection. We present cuMF_SGD, a CUDA-based SGD solution for large-scale MF problems. On a single CPU, we design two workload schedule schemes, i.e., batch-Hogwild! and wavefront-update, that fully exploit the massive amount of cores. batch-Hogwild! as a vectorized version of Hogwild! especially overcomes the issue of memory discontinuity. On three datasets with only one Maxwell or Pascal GPU, cuMF_SGD runs 3.1 to 28.2x as fast compared with state-of-art CPU solutions on 1 to 64 CPU nodes.


Additional Session Information
All
Talk
Accelerated Analytics
Higher Education / Research
25 minutes
Session Schedule