Research: Parallel Computing for Machine Learning
Project Information
- Discipline
- Computer Science (401)
- Subdiscipline
- 14.09 Computer Engineering
- Orientation
- Research
The goal of this project is to discover, understand, and exploit the available parallelism in machine learning algorithms as applied to big data problems.
Intellectual Meritexplore parallel computing for machine learning by working on real problems and designing parallel machine learning algorithms
Broader ImpactsThis project will provide invaluable research, educational and training opportunities to students at both undergraduate and graduate levels.
Project Contact
- Project Lead
- Wilson Rivera (wriverapr)
- Project Manager
- Wilson Rivera (wriverapr)
- Project Members
- Carlos Gomez, Oscar Gomez, Rogelio Vázquez, Jorge Perea, Henry Estepar, Andres Malines, Christian Montes, Juan Nieves, Omar Soto
Resource Requirements
- Hardware Systems
-
- alamo (Dell optiplex at TACC)
- india (IBM iDataPlex at IU)
- sierra (IBM iDataPlex at SDSC)
- delta (GPU Cloud)
We will use FutureGrid resources as a testbed.
Scale of UseAbout 10 research collaborators.
Project Timeline
- Submitted
- 06/11/2013 - 12:50