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

S7294 - Controlling Hundreds of GPU-Powered Plasma-Physics Simulations with Machine Learning Algorithms

Session Speakers
Session Description

Better hardware and algorithms have made plasma-physics particle-in-cell codes much faster. Instead of running individual simulations, it's now common to explore the space of physical parameters with large sets of simulations. However, predefined regularly spaced parameter scans can be inefficient and expensive. Instead, we use an adaptive algorithm that learns from previous simulations and determines the most promising parameters to try next. We illustrate this method on the problem of electron injection in laser-wakefield acceleration. Using hundreds of GPU-powered simulations with the code FBPIC on the Titan cluster at ORNL, the algorithm quickly focuses on the most relevant regions of the explored parameter space.


Additional Session Information
Intermediate
Talk
Computational Physics
Government / National Labs Higher Education / Research
25 minutes
Session Schedule