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

S7263 - Bayesian Inference and Markov Chain Monte Carlo Algorithms on GPUs

Session Speakers
Session Description

We'll discuss the Bayesian statistical paradigm and Markov Chain Monte Carlo (MCMC) algorithms - the cornerstone of modern Bayesian computation. Scalable MCMC for big datasets and complex models is currently an open research question. Using GPUs provides a promising and largely unexplored avenue for accelerating these algorithms, but is nontrivial, because MCMC is inherently sequential and has traditionally been considered difficult to parallelize. We'll show how Gibbs sampling, a widely used MCMC algorithm, can be effectively parallelized on GPUs for a large class of exchangeable hierarchical Bayesian models. Participants will learn the mathematical and hardware/software challenges in bringing GPUs to the Bayesian community. Background in Bayesian statistics or MCMC is not assumed.


Additional Session Information
Intermediate
Talk
Accelerated Analytics Algorithms Federal
Higher Education / Research
25 minutes
Session Schedule