mrgsim.parallel provides parallelized simulation workflows for use with mrgsolve models. Input data sets are chunked into lists and simulated in parallel using multiple new R sessions (new R processes started to handle each chunk) or forked R sessions (R processes forked from the current R process). This implies some not-insignificant overhead for the parallelization and therefore this strategy will only speed up problems with some size or complexity to them; you will see little performance gain and possibly some slowdown by trying to parallelize problems that are smaller or run very fast to begin with.
Because this vignette is embedded in the mrgsim.parallel R package, the code here must run fairly quickly. Even problems where there is a large speed up from parallelization might still take too much time to include in a package vignette. Therefore, we will mainly stick to examples of a much smaller scope. These examples are probably not big enough to see a tremendous performance gain; that is not the point. We simply want to illustrate the workflows to get you started simulating in parallel on appropriately-sized problems.
In this section, we will create a data set and then simulate it with and without parallelization.
library(mrgsim.parallel)
library(dplyr)We’ll simulate 100 subjects receiving each of 3 dose levels
data <- expand.ev(amt = c(100, 200, 300), ID = seq(100))
count(data, amt). amt n
. 1 100 100
. 2 200 100
. 3 300 100using the mrgsolve house model
mod <- house(end = 72)It is probably most efficient to just simulate this in one go
outx <- mrgsim_d(mod, data, output = "df")Here, we’ve called on mrgsolve:::mrgsim_d(), which takes the model object (mod) as the first argument and a data set (data) as the second argument.
mrgsim provides two parallelized versions of this function, one using a backend provided by the parallel package and another which taps the fuure and future.apply` packages for the parallel backend.
For platform independent parallelization, use fu_mrgsim_d() (fu stands for future) after setting the desired plan() for parallelization. For example, we can use future::multisession with 2 workers
future::plan(future::multisession, workers = 2L)out <- fu_mrgsim_d(mod, data, .seed = 123, nchunk = 6)Because the problem is so small, this would actually take longer to run than the non-parallelized version.
If you are on macos or unix operating system, you can parallelize using forked R processes. This generally runs much faster than future::multisession, which requires new R processes to be started up.
mc_mrgsim_d() will parallelize this simulation using forked processes using the parallel package
out <- mc_mrgsim_d(mod, data, mc.cores = 2, .seed = 123, nchunk = 6)Or this can be run using fu_mrgsim_d() with future::multicore, which also will parallelize across forked R processes
future::plan(future::multicore)
out <- fu_mrgsim_d(mod, data, .seed = 123, nchunk = 6)R processes cannot be forked on the Windows operating system, so you cannot parallelize with mc_mrgsim_d() or fu_mrgsim_d() + plan(multicore) on Windows.
The other workflow that provided is similar to mrgsolve::mrgsim_ei(), which simulates an idata_set with an event object. For example, we create an event object
e <- ev(amt = 100, ii = 24, addl = 27)and then a data frame with individual level parameters (in this case, we’ll simulate a bunch of weights)
idata <- data.frame(WT = runif(25, 40, 140))
head(idata). WT
. 1 49.18584
. 2 51.30360
. 3 82.85262
. 4 131.22516
. 5 118.51518
. 6 87.48769Then simulate
out <- mrgsim_ei(mod, e, idata)This already runs very fast. But let’s parallelize it anyway. First, use fu_mrgsim_ei()
future::plan(future::multisession, workers = 2L)
out <- fu_mrgsim_ei(mod, e, idata,.seed = 123, nchunks = 6)Or if you are on macos or unix operating system, use mc_mrgsim_ei() to invoke multicore parallelization provided by parallel
out <- mc_mrgsim_ei(mod, e, idata, .seed = 123, nchunks = 6, mc.cores = 2)To simulate in the “background”, we first launch another R process using callr::r_bg() and run a chunked simulation in that R process. Because it is in the background, we can get the R prompt back and query the simulation process. Once the process is done, we can collect the result. Let’s run an example.
The function is bg_mrgsim_d(). Like mc_mrgsim_d(), we have to pass in the model object and a data set
out <- bg_mrgsim_d(mod, data, nchunk = 2)Because this simulation is run in a package vignette, we’ll use the default of waiting for the simulation to finish. Once it is done, we have a process object that tells us the simulation is “done”.
out. PROCESS 'R', finished.To collect the result, run
sims <- out$get_result()And we have a list of simulated data
length(sims). [1] 2head(sims[[1]]). ID time GUT CENT RESP DV CP
. 1 1 0.00 0.00000 0.00000 50.00000 0.000000 0.000000
. 2 1 0.00 100.00000 0.00000 50.00000 0.000000 0.000000
. 3 1 0.25 74.08182 25.74883 48.68223 1.287441 1.287441
. 4 1 0.50 54.88116 44.50417 46.18005 2.225208 2.225208
. 5 1 0.75 40.65697 58.08258 43.61333 2.904129 2.904129
. 6 1 1.00 30.11942 67.82976 41.37943 3.391488 3.391488For very large simulations, we can write the simulated output to a data “locker” on disk and then read it in later. We’ll call the locker foo in the tempdir()
locker <- file.path(tempdir(), "foo")
out <- bg_mrgsim_d(mod, data, .locker = locker, nchunk = 4)Now, the output isn’t simulated data but file names where the data are stored
files <- out$get_result()
files. [[1]]
. [1] "/var/folders/5w/2ky5lwcj1zq7kyk4c3zg3zpw0000gp/T//RtmpuJ6pZt/foo/bg-1-4.fst"
.
. [[2]]
. [1] "/var/folders/5w/2ky5lwcj1zq7kyk4c3zg3zpw0000gp/T//RtmpuJ6pZt/foo/bg-2-4.fst"
.
. [[3]]
. [1] "/var/folders/5w/2ky5lwcj1zq7kyk4c3zg3zpw0000gp/T//RtmpuJ6pZt/foo/bg-3-4.fst"
.
. [[4]]
. [1] "/var/folders/5w/2ky5lwcj1zq7kyk4c3zg3zpw0000gp/T//RtmpuJ6pZt/foo/bg-4-4.fst"We can read the data back in with fst::read_fst()
library(fst)
sims <- lapply(files, read_fst)
head(sims[[2]]). ID time GUT CENT RESP DV CP
. 1 76 0.00 0.00000 0.00000 50.00000 0.000000 0.000000
. 2 76 0.00 100.00000 0.00000 50.00000 0.000000 0.000000
. 3 76 0.25 74.08182 25.74883 48.68223 1.287441 1.287441
. 4 76 0.50 54.88116 44.50417 46.18005 2.225208 2.225208
. 5 76 0.75 40.65697 58.08258 43.61333 2.904129 2.904129
. 6 76 1.00 30.11942 67.82976 41.37943 3.391488 3.391488Or use the internalize helper which returns a single data frame by default
sims <- internalize_fst(locker)
head(sims). # A tibble: 6 × 7
. ID time GUT CENT RESP DV CP
. <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
. 1 1 0 0 0 50 0 0
. 2 1 0 100 0 50 0 0
. 3 1 0.25 74.1 25.7 48.7 1.29 1.29
. 4 1 0.5 54.9 44.5 46.2 2.23 2.23
. 5 1 0.75 40.7 58.1 43.6 2.90 2.90
. 6 1 1 30.1 67.8 41.4 3.39 3.39The background workflow simulates each chunk sequentially by default. We can also parallelize this simulation by specifying a .plan
out <- bg_mrgsim_d(
mod,
data,
nchunk = 4,
.plan = "multisession",
.locker = locker,
.cores = 2
)
sims <- internalize_fst(locker)mrgsim.parallel also provides several tools that can make these workflows easier.
To chunk a data frame by rows, use chunk_by_row()
data <- data.frame(i = seq(10))
data_list <- chunk_by_row(data, nchunk = 2)The result is a list of data frames with the corresponding number of chunks
length(data_list). [1] 2data_list[[2]]. i
. 6 6
. 7 7
. 8 8
. 9 9
. 10 10Similarly, use chunk_by_id() which will look at a single column and chunk based on those values
set.seed(8789)
data <- data.frame(id = c(rep("a", 4), rep("b", 3), rep("c", 2), rep("d", 5)))
data. id
. 1 a
. 2 a
. 3 a
. 4 a
. 5 b
. 6 b
. 7 b
. 8 c
. 9 c
. 10 d
. 11 d
. 12 d
. 13 d
. 14 ddata_list <- chunk_by_id(data, id_col = "id", nchunk = 2)
data_list[[2]]. id
. 8 c
. 9 c
. 10 d
. 11 d
. 12 d
. 13 d
. 14 dThere is another set of tools that help you systematically save large simulation outputs to disk in a format that can be quickly and flexibly read back in later. This workflow is called file stream and is described in another vignette.