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

S7526 - Scaling Deep Learning on High Performance Computers for Use in Scientific Workloads

Session Speakers
Session Description

Deep learning has become a popular tool for insight on problems where deterministic models don't yet exist. Recent development of deep learning frameworks using GPUs has allowed the application of deep learning to problems where fast solutions are required. The scientific community has traditionally sought to develop deterministic models to describe physical phenomena, using highly scalable systems to simulate problems with ever increasing fidelity. While many science domains have developed robust predictive methods, there are still problems lacking models that can describe observed phenomena. In many of these cases, the problem may contain unknown variables, or be fundamentally hard to solve, where the simulation cannot fully predict observations. These areas include biological systems, chaotic systems, and medical research. There are also fields where a priori models do exist, but surveying the parameter space through simulation of large datasets would have very long time-to-solutions. These areas include instrument data analysis and materials by design. We'll explore how the scientific community is using deep learning to conduct leading-edge research outside of traditional modeling techniques. We'll also explore opportunities and obstacles to scaling deep learning workloads on high performance computing systems.


Additional Session Information
All
Panel
Deep Learning and AI HPC and Supercomputing
Higher Education / Research
50 minutes
Session Schedule