Performance benefit by using compiled model definitions in mkin • mkin

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How to benefit from compiled models -

When 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")

Comparison with other solution methods -

First, we build a simple degradation model for a parent compound with -one metabolite, and we remove zero values from the dataset.

-library("mkin", quietly = TRUE)
-SFO_SFO <- mkinmod(
-  parent = mkinsub("SFO", "m1"),
-  m1 = mkinsub("SFO"))

## Temporary DLL for differentials generated and loaded

-FOCUS_D <- subset(FOCUS_2006_D, value != 0)

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. Since mkin version 0.9.49.11, an analytical -solution is also implemented, which is included in the tests below.

-if (require(rbenchmark)) {
-  b.1 <- benchmark(
-    "deSolve, not compiled" = mkinfit(SFO_SFO, FOCUS_D,
-       solution_type = "deSolve",
-       use_compiled = FALSE, quiet = TRUE),
-    "Eigenvalue based" = mkinfit(SFO_SFO, FOCUS_D,
-       solution_type = "eigen", quiet = TRUE),
-    "deSolve, compiled" = mkinfit(SFO_SFO, FOCUS_D,
-       solution_type = "deSolve", quiet = TRUE),
-    "analytical" = mkinfit(SFO_SFO, FOCUS_D,
-       solution_type = "analytical",
-       use_compiled = FALSE, quiet = TRUE),
-    replications = 1, order = "relative",
-    columns = c("test", "replications", "relative", "elapsed"))
-  print(b.1)
-} else {
-  print("R package rbenchmark is not available")
-}

##                    test replications relative elapsed
-## 4            analytical            1    1.000   0.105
-## 3     deSolve, compiled            1    1.276   0.134
-## 2      Eigenvalue based            1    1.762   0.185
-## 1 deSolve, not compiled            1   21.914   2.301

We see that using the compiled model is by more than a factor of 10 -faster than using deSolve without compiled code.

Model without analytical solution -

This evaluation is also taken from the example section of mkinfit. No -analytical solution is available for this system, and now Eigenvalue -based solution is possible, so only deSolve using with or without -compiled code is available.

-if (require(rbenchmark)) {
-  FOMC_SFO <- mkinmod(
-    parent = mkinsub("FOMC", "m1"),
-    m1 = mkinsub( "SFO"))
-
-  b.2 <- benchmark(
-    "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_D,
-                                      use_compiled = FALSE, quiet = TRUE),
-    "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE),
-    replications = 1, order = "relative",
-    columns = c("test", "replications", "relative", "elapsed"))
-  print(b.2)
-  factor_FOMC_SFO <- round(b.2["1", "relative"])
-} else {
-  factor_FOMC_SFO <- NA
-  print("R package benchmark is not available")
-}

## Temporary DLL for differentials generated and loaded

##                    test replications relative elapsed
-## 2     deSolve, compiled            1    1.000   0.176
-## 1 deSolve, not compiled            1   23.938   4.213

Here we get a performance benefit of a factor of 24 using the version -of the differential equation model compiled from C code!

This vignette was built with mkin 1.2.3 on

## R version 4.2.3 (2023-03-15)
-## Platform: x86_64-pc-linux-gnu (64-bit)
-## Running under: Debian GNU/Linux 12 (bookworm)

## CPU model: AMD Ryzen 9 7950X 16-Core Processor

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Performance benefit by using compiled model -definitions in mkin

Johannes -Ranke

2023-04-16

How to benefit from compiled models -

Comparison with other solution methods -

Model without analytical solution -