Research: Parallel Computing for Machine Learning

Project Information

Discipline
Computer Science (401) 
Subdiscipline
14.09 Computer Engineering 
Orientation
Research 
Abstract

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 Merit

explore parallel computing for machine learning by working on real problems and designing parallel machine learning algorithms

Broader Impacts

This 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)
 
Use of FutureGrid

We will use FutureGrid resources as a testbed.

Scale of Use

About 10 research collaborators.

Project Timeline

Submitted
06/11/2013 - 12:50 
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