Big Data vs Big Compute

Come for Table discussions, Member Self-Intro, What's New, Application Showcase, and Advanced Application Development Techniques! Exchange ideas, meet experts, share code... all HPC & GPU, all practical, all cutting-edge.

Agenda:

General Discussions:

6:15-6:30pm What’s new in HPC & GPU Supercomputing

6:30-6:45pm Member self-intros: 30 seconds for each member

Main Program:

6:45-7:40pm Big Data Talk: Large Scale Machine Learning on Hadoop and HBase (Aaron Kimball)

7:40-7:45pm Break

7:45-8:30pm Panel Discussion: Big Data vs Big Compute - A Discussion on the Similarities and Differences

Panelists:

Aaron Kimball, co-founder of WibiData

Steve Scott, Tesla CTO, NVIDIA

Tim Kaldewey, IBM Research (Author of GPU Computing Gems, Jade Edition, Chapter 1)

Tim Child, Chief Entrepeneur Officer at 3DMashUp

Main Talk:

Large Scale Machine Learning on Hadoop and HBase

Abstract:

Using modern data collection techniques, organizations are increasingly capable of collecting very large data sets about their users. These data sets can be applied toward a number of user-centric analyses. Examples of this are product recommendations; predicting missing links in a social network graph; or providing personalized deals or offers. The state of the art when performing this at scale is to use Hadoop MapReduce for processing and HBase or HDFS for storage of the collected user data and results.

This talk discusses a large-scale user-centric analysis within the MapReduce framework in a case study of a new system we have developed called WibiData.

Bio:

Aaron is the Founder and CTO of WibiData, Inc., a software company that engineers solutions for the large-scale user-centric data challenges that face today’s enterprises. He is a committer on the Apache Hadoop project and has been working with Hadoop since 2007. Aaron previously worked at Cloudera, a company which provides an enterprise platform, support and services built around Hadoop. Aaron founded the open source Apache Sqoop data import tool and Apache MRUnit Hadoop testing library projects. Aaron holds a B.S. in Computer Science from Cornell University and a M.S. in Computer Science from the University of Washington.

Location:

Open space;
Carnegie Mellon Silicon Valley;
NASA Research Park Bldg 23;
Mountain View, CA 94043;

Directions (http://www.cmu.edu/silicon-valley/about-us/directions.html) to Carnegie Mellon Silicon Valley;

Google Map (http://maps.google.com/maps/ms?gl=us&hl=en&ie=UTF8&msa=0&ll=37.410941,-122.063169&spn=0.019191,0.048923&t=h&z=15&msid=215438781255871976989.00049cacf6f0e5596e5cc) showing parking, check point, and building entrance;

NOTE: You will need a government issued ID (e.g. Driver's License) to enter NASA Research Park

http://photos1.meetupstatic.com/photos/event/4/b/6/e/event_21799310.jpeg