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[farmshare-discuss] Reminder - MapReduce workshop and talks today through Wednesday

Alex Chekholko chekh at stanford.edu
Mon Apr 29 09:50:06 PDT 2013



-------- Original Message --------
Subject: [HPC] Reminder - MapReduce workshop and talks today through 
Wednesday

Good morning. Just a reminder that the ICME MapReduce Workshop is taking 
place today, tomorrow and Wednesday.  Room G19 Redwood.  Even if you 
don't have a specific problem to work on today, the morning talks may be 
of interest.

Regards,

Ruth Marinshaw
Stanford Research Computing

******************************************************************************************


2nd ICME Workshop on MapReduce in Science and Engineering
Stanford University
April 29 - May 1
www.stanford.edu/~paulcon/icme-mapreduce-2013

We would like to invite you to participate in the 2nd ICME MapReduce
Workshop to be held April 29 - May 1 on Stanford's campus.

The goals of the workshop are as follows:
- Learn the basics of Hadoop/MapReduce.
- Use Hadoop/MapReduce to process large-scale data from science and
engineering applications.
- Explore your own data in a collaborate, group setting on a Hadoop cluster.

Each day of the workshop will include a morning session and an afternoon
session. The morning sessions will take place in Redwood Hall, room G19,
and are open to the public. The schedule of speakers is as follows:

Monday, April 29:
9:30am -- Prof. David Gleich (Purdue computer science), Sparse matrix
computations in MapReduce
10:30am -- Austin Benson (Stanford ICME), Tall and skinny matrix
computations in MapReduce

Tuesday, April 30:
9:30am -- Joe Buck (UC Santa Cruz computer science), Extending MapReduce
for scientific computing
10:30am -- Dr. Chunsheng (Victor) Fang (Pivotal Data Science Team),
Large Scale Video Analytics Platform on Pivotal Hadoop

Wednesday, May 1:
9:30am -- Dr. Joe Nichols (Stanford mechanical engineering),
Computational fluid dynamics applications in MapReduce
10:30am -- Dr. Lavanya Ramakrishnan (Lawrence Berkeley National Labs),
Evaluating MapReduce and Hadoop for Science

In the afternoon, the project groups will work side-by-side writing
MapReduce scripts to perform their specific analysis tasks on their own
data set. Last year, the afternoon sessions also included a great deal
of informal discussion on the advantages and limitations of MapReduce
for scientific data sets.

To learn more and find out how you can participate, please visit our
website:
www.stanford.edu/~paulcon/icme-mapreduce-2013



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