Skip to contents

Parallel computing

Joel H. Nitta

04 October, 2022

Source: vignettes/parallel.Rmd
parallel.Rmd

One feature of canaper is the ability to use parallel computing (running calculations on multiple CPUs simultaneously) to speed up analysis. The parallel computing is used during the randomizations carried out by cpr_rand_test(), since this function involves calculating the same values on many random replicates. This vignette shows how and when to use parallel computing to speed up cpr_rand_test().

(This vignette assumes a basic understanding of CANAPE, community data matrices, and randomizations. If you aren’t familiar with any of these, you should probably see the the CANAPE example vignette first).

Let’s get started by loading the packages used in this vignette.

library(canaper) # This package
library(tictoc) # For timing
library(future) # For parallel computing
# Set seed for random number generator for reproducible results
set.seed(12345)

How to parallelize

canaper uses the future package to handle parallel computing. In future, specification of sequential (i.e., no parallel computing) vs. parallel computing, and the number of CPUs (i.e., “cores”) to use in parallel is specified outside of other functions. This is easiest to see with an example.

Sequential mode

First, let’s run an analysis in default sequential mode (without parallel computing). I’ll use the tictoc package to time how long it takes to run.

tic()
biod_res_seq <- cpr_rand_test(
  biod_example$comm, biod_example$phy,
  null_model = "swap", n_reps = 50
)
toc()
#> 2.836 sec elapsed

Since we have specified 50 random replicates and are not using parallel computing, cpr_rand_test() calculated the various phylogenetic diversity metrics for each of the 50 replicates one at a time.

Parallel mode

Before trying the parallel version, let’s check how many CPUs are available to use:

availableCores()
#> system 
#>      6

OK, we have verified that there are multiple cores available for parallel computing.

To enable parallel computing, just add one line before cpr_rand_test(): plan(multisession, workers = 2) 1. Here, the multisession, workers = 2 part is telling future that we want to use 2 CPUs in parallel on our local machine. See future::plan() for other options. Otherwise, everything is the same.

# Set up parallel computing with 2 CPUs
plan(multisession, workers = 2)

tic()
biod_res_par <- cpr_rand_test(
  biod_example$comm, biod_example$phy,
  null_model = "swap", n_reps = 50
)
toc()
#> 7.699 sec elapsed

# Change back to default sequential mode
plan(sequential)

This time, the calculations were carried out in 2 batches in parallel.

But there is no significant improvement in processing time2. What is going on here?

When to parallelize?

Although it may seem to always be a good idea to speed things up by using parallel computing, this is not the case. There is some computational overhead involved in splitting the job across multiple processes, coordinating those processes, and putting everything back together again.

If your dataset is small, this overhead may outweigh simply running the analysis sequentially. That is the case with the biod_example data. Let’s check the size of this dataset:

# dim() returns number of rows, then columns
dim(biod_example$comm)
#> [1] 127  31

The biod_example dataset is small because it is entirely made-up and used only for testing code (and we want tests to run quickly).

Let’s see how that compares with another dataset included in canaper, the acacia dataset. The acacia dataset is “real-life” data of the genus Acacia in Australia:

# dim() returns number of rows, then columns
dim(acacia$comm)
#> [1] 3037  508

Quite a bit larger!

Let’s see how parallel computing works on the acacia dataset:

plan(sequential)
tic()
acacia_res_seq <- cpr_rand_test(
  acacia$comm, acacia$phy,
  null_model = "curveball", n_reps = 100
)
#> Warning: 'comm' is > 95% absences (zeros). Be sure that 'n_reps' and
#> 'n_iterations' are sufficiently large to ensure adequate mixing of random
#> communities
#> Warning in match_phylo_comm(phy = phy, comm = comm): Dropping tips from the tree because they are not present in the community data: 
#>  Pararchidendron_pruinosum, Paraserianthes_lophantha
toc()
#> 61.353 sec elapsed
# Run cpr_rand_test() in parallel with 2 CPUs
plan(multisession, workers = 2)
tic()
acacia_par_seq <- cpr_rand_test(
  acacia$comm, acacia$phy,
  null_model = "curveball", n_reps = 100
)
#> Warning: 'comm' is > 95% absences (zeros). Be sure that 'n_reps' and
#> 'n_iterations' are sufficiently large to ensure adequate mixing of random
#> communities
#> Warning in match_phylo_comm(phy = phy, comm = comm): Dropping tips from the tree because they are not present in the community data: 
#>  Pararchidendron_pruinosum, Paraserianthes_lophantha
toc()
#> 34.091 sec elapsed
plan(sequential)

And now we start to see the performance improvements that be can be gained from parallel computing!3

Progress bars

If you’d like to track the progress of cpr_rand_test() in real time, you can enable a progress bar. There are two ways to do so (neither of these will show up on the webpage, but do try it at home!). Similar to parallelization, this is done outside of the actual function.

One way is to add progressr::handlers(global = TRUE) before cpr_rand_test():

progressr::handlers(global = TRUE)

biod_res_long <- cpr_rand_test(
  biod_example$comm, biod_example$phy,
  null_model = "swap", n_reps = 500
)

The other way is to place cpr_rand_test() inside the progressr::with_progress() function:

progressr::with_progress(
  biod_res_long <- cpr_rand_test(
    biod_example$comm, biod_example$phy,
    null_model = "swap", n_reps = 500
  )
)

Conclusion

This vignette shows how easy it is to enable parallel computing in canaper, and when it makes sense to do so. I hope it helps your analyses run faster!