A perfect world would let us stop time to research and write, since a technical book covers a moving target. We didn’t have such a luxury, so instead we set aside some space to pick up on some new arrivals.
This chapter mentions a few tools for which we could have provided more coverage, had we been willing to postpone the book’s release date. Think of this as a look into one possible future of R parallelism. Special thanks to our colleagues, reviewers, and friends who so kindly brought these to our attention.
foreach() function executes an arbitrary R expression across an input.
foreach()’s strength is
that it can execute in parallel with the help of a
supplied parallel backend. The
package provides such a backend, using the Redis datastore as a job queue.
doRedis can work locally to take
advantage of multicore systems, and also farm tasks out to remote R
instances (“workers”). It’s straightforward to add or remove workers at
runtime—even in mid-job—to adapt to changing work conditions or speed up
job processing. Similar to Hadoop,
doRedis is fault-tolerant in that failed tasks
are automatically resubmitted to their job queue.
doRedis supports Linux, Mac OS X,
and Windows systems.
Revolution Analytics is a company that provides R tools, support, and training. They have two products of note.
First up is the ...