Note: This vignette presents some performance tests ran between non-parallel and parallel versions of fundiversity
functions. Note that to avoid the dependency on other packages, this vignette is pre-computed.
Within fundiversity
the computation of most indices can be parallelized using the future
package. The goal of this vignette is to explain how to toggle and use parallelization in fundiversity
. The functions that currently support parallelization are summarized in the table below:
| Function Name | Index Name | Parallelizable[^1] | Memoizable[^2] |
|:----------------------|:---------------|:------------------:|:--------------:|
| fd_fric()
| FRic | ✅ | ✅ |
| fd_fric_intersect()
| FRic_intersect | ✅ | ✅ |
| fd_fdiv()
| FDiv | ✅ | ✅ |
| fd_feve()
| FEve | ✅ | ❌ |
| fd_fdis()
| FDis | ✅ | ❌ |
| fd_raoq()
| Rao's Q | ❌ | ❌ |
[^1]: parallelization through the future
backend please refer to the parallelization vignette for details.
[^2]: memoization means that the results of the functions calls are cached and not recomputed when recalled, to toggle it off see the fundiversity::fd_fric()
Details section.
Note that memoization and parallelization cannot be used at the same time. If the option fundiversity.memoise
has been set to TRUE
but the computations are parallelized, fundiversity
will use unmemoised versions of functions.
The future
package provides a simple and general framework to allow asynchronous computation depending on the resources available for the user. The first vignette of future
gives a general overview of all its features. The main idea being that the user should write the code once and that it would run seamlessly sequentially, or in parallel on a single computer, or on a cluster, or distributed over several computers. fundiversity
can thus run on all these different backends following the user's choice.
library("fundiversity") data("traits_birds", package = "fundiversity") data("site_sp_birds", package = "fundiversity")
By default the fundiversity
code will run sequentially on a single core. To trigger parallelization the user needs to define a future::plan()
object with a parallel backend such as future::multisession
to split the execution across multiple R sessions.
# Sequential execution fric1 <- fd_fric(traits_birds) # Parallel execution future::plan(future::multisession) # Plan definition fric2 <- fd_fric(traits_birds) # The code resolve in similar fashion identical(fric1, fric2) #> [1] TRUE
Within the future::multisession
backend you can specify the number of cores on which the function should be parallelized over through the argument workers
, you can change it in the future::plan()
call:
future::plan(future::multisession, workers = 2) # Only 2 cores are used fric3 <- fd_fric(traits_birds) identical(fric3, fric2) #> [1] TRUE
To learn more about the different backends available and the related arguments needed, please refer to the documentation of future::plan()
and the overview vignette of future
.
We can now compare the difference in performance to see the performance gain thanks to parallelization:
future::plan(future::sequential) non_parallel_bench <- microbenchmark::microbenchmark( non_parallel = { fd_fric(traits_birds) }, times = 20 ) future::plan(future::multisession) parallel_bench <- microbenchmark::microbenchmark( parallel = { fd_fric(traits_birds) }, times = 20 ) rbind(non_parallel_bench, parallel_bench) #> Unit: milliseconds #> expr min lq mean median uq max neval cld #> non_parallel 8.9509 9.2691 14.93812 13.32405 18.4841 33.153 20 a #> parallel 224.7037 248.9997 345.59427 274.59615 304.6889 1660.164 20 b
The non parallelized code runs faster than the parallelized one! Indeed, the parallelization in fundiversity
parallelize the computation across different sites. So parallelization should be used when you have many sites on which you want to compute similar indices.
# Function to make a bigger site-sp dataset make_more_sites <- function(n) { site_sp <- do.call(rbind, replicate(n, site_sp_birds, simplify = FALSE)) rownames(site_sp) <- paste0("s", seq_len(nrow(site_sp))) site_sp }
For example with a dataset 5000 times bigger:
bigger_site <- make_more_sites(5000) microbenchmark::microbenchmark( seq = { future::plan(future::sequential) fd_fric(traits_birds, bigger_site) }, multisession = { future::plan(future::multisession, workers = 4) fd_fric(traits_birds, bigger_site) }, multicore = { future::plan(future::multicore, workers = 4) fd_fric(traits_birds, bigger_site) }, times = 20 ) #> Unit: seconds #> expr min lq mean median uq max neval cld #> seq 78.13766 195.17853 184.92560 196.89360 197.90500 200.56116 20 a #> multisession 34.23402 54.44036 53.39172 54.88206 55.19359 61.83829 20 b #> multicore 75.43857 192.45136 183.07222 196.48277 201.16889 209.39847 20 a
Session info of the machine on which the benchmark was ran and time it took to run
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