mrgsolve.parallel facilitates parallel simulation with mrgsolve in R. The future and parallel packages provide the parallelization.
There are 2 main workflows:
- Split a
data_setinto chunks by ID, simulate the chunks in parallel, then assemble the results back to a single data frame. - Split an
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.
library(dplyr)
library(future)
library(mrgsim.parallel)
options(future.fork.enable = TRUE, parallelly.fork.enable = TRUE, mc.cores = 4L)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 CL
. 1 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
plan(multisession, workers = 6)
mod <- modlib("pk1cmt", end = 168*4, delta = 1)For this workflow, we have a set of parameters (idata) along with an
event object that gets applied to all of the parameters
idata <- tibble(CL = runif(4000, 0.5, 1.5), ID = seq_along(CL))
head(idata). # 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 evid
. 1 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
dose <- ev_seq(ev(amt = 100), ev(amt = 50, ii = 12, addl = 2))
dose <- ev_rep(dose, 1:5)
dose. ID time amt ii addl cmt evid
. 1 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 evid
. 1 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
.
. $`2`
. ID time amt ii addl cmt evid
. 7 4 0 100 0 0 1 1
. 8 4 0 50 12 2 1 1
. 9 5 0 100 0 0 1 1
. 10 5 0 50 12 2 1 1
See also: chunk_by_row
plan(transparent)
system.time(x <- fu_mrgsim_d(mod, data, nchunk = 8, .dry = TRUE)). user system elapsed
. 0.014 0.001 0.016
plan(multisession, workers = 8L)
system.time(x <- fu_mrgsim_d(mod, data, nchunk = 8, .dry = TRUE)). user system elapsed
. 0.045 0.003 5.151
First check the range of times from the previous example
summary(ans1$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.