title: Create a cluster of co-workers' Windows 7 PCs for parallel processing in R?
body: <p>I am running the termstrc yield curve analysis package in R across 10 years of daily bond price data for 5 different countries. This is highly compute intensive, it takes 3200 seconds per country on a standard lapply, and if I use foreach and %dopar% (with doSNOW) on my 2009 i7 mac, using all 4 cores (8 with hyperthreading) I get this down to 850 seconds. I need to re-run this analysis every time I add a country (to compute inter-country spreads), and I have 19 countries to go, with many more credit yield curves to come in the future. The time taken is starting to look like a major issue. By the way, the termstrc analysis function in question is accessed in R but is written in C. </p>
  
  <p>Now, we're a small company of 12 people (read limited budget), all equipped with 8GB ram, i7 PCs, of which at least half are used for mundane word processing / email / browsing style tasks, that is, using 5% maximum of their performance. They are all networked using gigabit (but not 10-gigabit) ethernet. </p>
  
  <p>Could I cluster some of these underused PCs using MPI and run my R analysis across them? Would the network be affected? Each iteration of the yield curve analysis function takes about 1.2 seconds so I'm assuming that if the granularity of parallel processing is to pass a whole function iteration to each cluster node, 1.2 seconds should be quite large compared with the gigabit ethernet lag?</p>
  
  <p>Can this be done? How? And what would the impact be on my co-workers. Can they continue to read their emails while I'm taxing their machines? </p>
  
  <p>I note that Open MPI seems not to support Windows anymore, while MPICH seems to. Which would you use, if any?</p>
  
  <p>Perhaps run an Ubuntu virtual machine on each PC?</p>