CODEWITHSUNDEEP
rforeachparallel-processingparallel.foreach
lemmingxuan
lemmingxuan

Reputation: 709

Parallelizing nested foreach loops

The documentation mentions, "When parallelizing nested for loops, there is always a question of which loop to parallelize. The standard advice is to parallelize the outer loop."

However, I did not see any specific examples of parallelizing the outer loops and then sequentially executing the inner loops. In such a case, would it be appropriate to use foreach+%dopar% for the outer loops and a regular for loop for the inner loops?

Furthermore:

x <-
  foreach(b=bvec, .combine='cbind') %:%
    foreach(a=avec, .combine='c') %dopar% {
      sim(a, b)
    }

This code block uses nested foreach, so does it mean that both the inner loops and outer loops are parallelized? This is a bit confusing to me.

I have two interpretations:

  1. The task of "taking a value from bvec, such as 1, and iterating over all values in avec" is divided among multiple threads to be executed simultaneously. Then, the task of "taking 2 from bvec and iterating over all values in avec" is also divided among multiple threads to be executed simultaneously...

  2. Are multiple threads executing sim(a, b) concurrently? If this is the case, wouldn't there be potential for confusion when collecting the results? (Although it seems that the order does not matter here.)

Which interpretation is correct?

Upvotes: 2

Views: 229

Answers (1)

Jinjin
Jinjin

Reputation: 610

Q1. %:% essentially transform multiple loops into a single one, like first calculating the full combination of bvec and avec, and then loop at one go. It is like:

foreach(a= interaction(bvec, avec), .combine='c') %dopar% { ...}
#not the source code, but you see what I mean.

This is different from the standard loop like below

  foreach(b=bvec, .combine='c') %do%{
    foreach(a=avec, .combine='c') %dopar% {...}}

In this case, the parallel is only happening in the inner loop, and outer loop must wait each value in bvec to finish and get combined before moving into the next value in bvec. Therefore, the larger-number iterator should be in the parallel processing (here avec for the inner loop), so the effeciency will be good. Otherwise, an extreme case can be if avec only have 1 value, and whole process is sequential. Now you can think about this question you have

would it be appropriate to use foreach+%dopar% for the outer loops and a regular for loop for the inner loops?

It is really dependent on the size of the iterator for each layer of loop rather than simply outer or inner. This is why %:% is useful here by converting nested loops into a single flat one irrespectively, so you don't need to think about the iterator size and layers.

Q2. Yes, all multiple threads executing sim(a, b) concurrently. To store the result, foreach will first define a list with its length equal to the sum number of (nested) loops, so the places for the results from all loops are reserved. Then the results can go into the right place of the list at any time, and there won't be any

"potential for confusion when collecting the results"

Upvotes: 1

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