mrgsolve.parallel facilitates parallel simulation with mrgsolve in R. The future and parallel packages provide the parallelization.
There are 2 main workflows:
data_set
into chunks by ID, simulate the chunks in parallel, then assemble the results back to a single data frame.idata_set
(individual-level parameters) into chunks by row, simulate the chunks in parallel, then assemble the results back to a single data frame.The nature of the parallel backend requires some overhead to get the parallel simulation done. So, it will take a reasonably-sized job to see a speed increase and small jobs will likely take longer with parallelization. But jobs taking more than a handful of seconds could benefit from this type of parallelization.
mod <- modlib("pk2cmt", end = 168*8, delta = 1)
data <- expand.ev(amt = 100*seq(1,2000), ii = 24, addl = 27*2+2)
data <- mutate(data, CL = runif(n(), 0.7, 1.3))
head(data)
. ID time amt ii addl cmt evid CL1 1 0 100 24 56 1 1 0.9714138
. 2 2 0 200 24 56 1 1 0.7977687
. 3 3 0 300 24 56 1 1 0.7937479
. 4 4 0 400 24 56 1 1 1.0287696
. 5 5 0 500 24 56 1 1 1.1802787
. 6 6 0 600 24 56 1 1 0.7458695 .
dim(data)
1] 2000 8 . [
We can simulate in parallel with the future package or the parallel package like this:
plan(multisession, workers = 4L)
system.time(ans1 <- future_mrgsim_d(mod, data, nchunk = 4L))
. user system elapsed 0.473 0.176 4.173 .
plan(multicore, workers = 4L)
system.time(ans1b <- future_mrgsim_d(mod, data, nchunk = 4L))
. user system elapsed 5.322 0.544 1.846 .
system.time(ans2 <- mc_mrgsim_d(mod, data, nchunk = 4L))
. user system elapsed 5.289 0.563 1.756 .
To compare an identical simulation done without parallelization
system.time(ans3 <- mrgsim_d(mod,data))
. user system elapsed 4.839 0.105 4.954 .
identical(ans2,as.data.frame(ans3))
1] TRUE . [
Backend and the model
For this workflow, we have a set of parameters (idata
) along with an event object that gets applied to all of the parameters
# A tibble: 6 × 2
.
. CL ID<dbl> <int>
. 1 0.552 1
. 2 0.765 2
. 3 0.669 3
. 4 0.943 4
. 5 0.929 5
. 6 1.19 6 .
dose <- ev(amt = 100, ii = 24, addl = 27)
dose
:
. Events
. time amt ii addl cmt evid1 0 100 24 27 1 1 .
Run it in parallel
system.time(ans1 <- mc_mrgsim_ei(mod, dose, idata, nchunk = 6))
. user system elapsed 3.705 0.481 1.486 .
And without parallelization
system.time(ans2 <- mrgsim_ei(mod, dose, idata, output = "df"))
. user system elapsed 3.313 0.076 3.395 .
identical(ans1,ans2)
1] TRUE . [
You can access the chunking functions for your own parallel workflows
. ID time amt ii addl cmt evid1 1 0 100 0 0 1 1
. 2 1 0 50 12 2 1 1
. 3 2 0 100 0 0 1 1
. 4 2 0 50 12 2 1 1
. 5 3 0 100 0 0 1 1
. 6 3 0 50 12 2 1 1
. 7 4 0 100 0 0 1 1
. 8 4 0 50 12 2 1 1
. 9 5 0 100 0 0 1 1
. 10 5 0 50 12 2 1 1 .
chunk_by_id(dose, nchunk = 2)
$`1`
.
. ID time amt ii addl cmt evid1 1 0 100 0 0 1 1
. 2 1 0 50 12 2 1 1
. 3 2 0 100 0 0 1 1
. 4 2 0 50 12 2 1 1
. 5 3 0 100 0 0 1 1
. 6 3 0 50 12 2 1 1
.
. $`2`
.
. ID time amt ii addl cmt evid7 4 0 100 0 0 1 1
. 8 4 0 50 12 2 1 1
. 9 5 0 100 0 0 1 1
. 10 5 0 50 12 2 1 1 .
See also: chunk_by_row
plan(transparent)
system.time(x <- fu_mrgsim_d(mod, data, nchunk = 8, .dry = TRUE))
. user system elapsed 0.014 0.001 0.016 .
plan(multisession, workers = 8L)
system.time(x <- fu_mrgsim_d(mod, data, nchunk = 8, .dry = TRUE))
. user system elapsed 0.045 0.003 5.151 .
First check the range of times from the previous example
summary(ans1$time)
. Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0 167.0 335.5 335.5 504.0 672.0 .
The post-processing function has arguments the simulated data and the model object
post <- function(sims, mod) {
filter(sims, time > 600)
}
dose <- ev(amt = 100, ii = 24, addl = 27)
ans3 <- mc_mrgsim_ei(mod, dose, idata, nchunk = 6, .p = post)
summary(ans3$time)
. Min. 1st Qu. Median Mean 3rd Qu. Max. 601.0 618.8 636.5 636.5 654.2 672.0 .
The main use case here is to summarize or some how decrease the volume of data before returning the combined simulations. In case memory is able to handle the simulation volume, this post-processing could be done on the combined data as well.
See inst/docs/stories.md (on GitHub only) for more details.