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Activity Number: 260
Type: Contributed
Date/Time: Monday, August 5, 2013 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Education
Abstract - #309493
Title: Classroom Demonstrations of Parallel Processing for Statistics
Author(s): Eric Suess*+
Companies: CSU East Bay
Keywords: R ; parallel processing ; mean and standard deviation ; linear regression ; bootstrapping ; clustering
Abstract:

With the common availability of multicore and multiprocessor computers, students of statistics have the ability to run statistical computations in parallel. Within R there are many packages to implement parallel processing and there are many packages that have built in options to run in parallel.

We introduce parallel processing for the mean and standard deviation. These examples show the idea of dividing the overall computation in to separate calculations that can be done in parallel. Other examples include, running linear regression calculations in parallel, using the option in the bootstrap command to run parallel bootstrap calculations, and to run clustering in parallel.

To show the benefits parallel processing we examine the time taken to perform the calculations. Not all statistical calculations benefit from parallel processing, but many do.


Authors who are presenting talks have a * after their name.

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