vignettes/web_only/compiled_models.Rmd
compiled_models.RmdWhen using an mkin version equal to or greater than 0.9-36 and a C compiler is available, you will see a message that the model is being compiled from autogenerated C code when defining a model using mkinmod. Starting from version 0.9.49.9, the mkinmod() function checks for presence of a compiler using
pkgbuild::has_compiler()
In previous versions, it used Sys.which("gcc") for this check.
On Linux, you need to have the essential build tools like make and gcc or clang installed. On Debian based linux distributions, these will be pulled in by installing the build-essential package.
On MacOS, which I do not use personally, I have had reports that a compiler is available by default.
On Windows, you need to install Rtools and have the path to its bin directory in your PATH variable. You do not need to modify the PATH variable when installing Rtools. Instead, I would recommend to put the line
Sys.setenv(PATH = paste("C:/Rtools/bin", Sys.getenv("PATH"), sep=";"))
into your .Rprofile startup file. This is just a text file with some R code that is executed when your R session starts. It has to be named .Rprofile and has to be located in your home directory, which will generally be your Documents folder. You can check the location of the home directory used by R by issuing
Sys.getenv("HOME")
First, we build a simple degradation model for a parent compound with one metabolite.
library("mkin", quietly = TRUE) SFO_SFO <- mkinmod( parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
## Successfully compiled differential equation model from auto-generated C code.We can compare the performance of the Eigenvalue based solution against the compiled version and the R implementation of the differential equations using the benchmark package. In the output of below code, the warnings about zero being removed from the FOCUS D dataset are suppressed.
if (require(rbenchmark)) { b.1 <- benchmark( "deSolve, not compiled" = mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", use_compiled = FALSE, quiet = TRUE), "Eigenvalue based" = mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet = TRUE), "deSolve, compiled" = mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet = TRUE), replications = 3) print(b.1) factor_SFO_SFO <- round(b.1["1", "relative"]) } else { factor_SFO_SFO <- NA print("R package rbenchmark is not available") }
## test replications elapsed relative user.self sys.self
## 3 deSolve, compiled 3 0.997 1.000 0.997 0.000
## 1 deSolve, not compiled 3 24.417 24.490 24.405 0.001
## 2 Eigenvalue based 3 1.159 1.162 1.159 0.000
## user.child sys.child
## 3 0 0
## 1 0 0
## 2 0 0We see that using the compiled model is by a factor of around 24 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs.
This evaluation is also taken from the example section of mkinfit.
if (require(rbenchmark)) { FOMC_SFO <- mkinmod( parent = mkinsub("FOMC", "m1"), m1 = mkinsub( "SFO")) b.2 <- benchmark( "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, use_compiled = FALSE, quiet = TRUE), "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE), replications = 3) print(b.2) factor_FOMC_SFO <- round(b.2["1", "relative"]) } else { factor_FOMC_SFO <- NA print("R package benchmark is not available") }
## Successfully compiled differential equation model from auto-generated C code.## test replications elapsed relative user.self sys.self
## 2 deSolve, compiled 3 1.392 1.000 1.391 0
## 1 deSolve, not compiled 3 43.021 30.906 43.002 0
## user.child sys.child
## 2 0 0
## 1 0 0Here we get a performance benefit of a factor of 31 using the version of the differential equation model compiled from C code!
This vignette was built with mkin 0.9.50.2 on
## R version 4.0.0 (2020-04-24)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 10 (buster)## CPU model: AMD Ryzen 7 1700 Eight-Core Processor