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library("fundiversity")

data("traits_birds", package = "fundiversity")
data("site_sp_birds", package = "fundiversity")

Within fundiversity the computation of most indices can be parallelized using the future package. The indices that currently support parallelization are: FRic, FDis, FDiv, and FEve. The goal of this vignette is to explain how to toggle and use parallelization in fundiversity.

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.

Running code in parallel

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.

Performance comparison

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: microseconds
#>          expr     min       lq     mean   median       uq     max neval
#>  non_parallel 514.915 539.2130 623.7170 563.7615 633.5185 975.669    20
#>      parallel 472.501 476.6675 504.0272 482.8980 493.9275 817.955    20

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
#>           seq 4.954230 5.070038 5.142720 5.173176 5.198141 5.311713    20
#>  multisession 6.300440 6.487385 6.549981 6.563890 6.607202 6.739610    20
#>     multicore 4.880869 5.025615 5.111846 5.101368 5.202465 5.489126    20

Session info of the machine on which the benchmark was ran and time it took to run

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