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

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

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

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.


Ruth Marinshaw
Stanford Research Computing


2nd ICME Workshop on MapReduce in Science and Engineering
Stanford University
April 29 - May 1

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

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