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R workshop XX: Parallel Computing with R

Contributed by 

Yuan Huang, PM Intern of SupStat Inc, 

Tong He, Data Scientist of SupStat Inc.

Vivian Zhang, CTO of SupStat Inc, will deliver this workshop.


Our slides can be found: 

We will go over the steps toward parallel computing.

1.Whether the problem is parallel-able ?

2.Tips to improve the parallel computing's efficiency.

3.Implementation in R.

We will discuss how to do load balance, how to reduce parallel over-head, how to make sure each nodes have different random number and the few statistical models to be paralleled. 

And do a overview of 

1.Rmpi ( R interface to MPI; flexible; powerful, but more complex.)

2.Snow (will be used for backends with foreach package today)

3.multicore (work only on a single node and Linux-like machine)

4.parallel (hybrid package containing snow and multicore)

5.foreach (parallel backends doSNOW / doMPI / doMC)

In the end,  we will give examples by using foreach package:

1. Bootstrapping: calculate CI for median.

2.Random Forest

3. Calculate the pairwise distance

4.Cross Validataion

5. Web scrapper

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