-How to benefit from compiled models

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.

+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=";"))

+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")

+Sys.getenv("HOME")

-Comparison with other solution methods

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

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)

+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   0.182
-## 3     deSolve, compiled            1    1.824   0.332
-## 2      Eigenvalue based            1    2.082   0.379
-## 1 deSolve, not compiled            1   46.181   8.405

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")
+}

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

Benchmark timings for mkin

Johannes Ranke

Last change 14 July 2022 (rebuilt 2022-09-16)

Last change 14 July 2022 (rebuilt 2022-11-01)

Performance benefit by using compiled model definitions in mkin

Johannes Ranke

Johannes Ranke

2021-02-15

2022-11-01

-How to benefit from compiled models

How to benefit from compiled models +

-Comparison with other solution methods

Comparison with other solution methods +

-Model without analytical solution

Model without analytical solution +

Short demo of the multistart method

Johannes Ranke

Last change 26 September 2022 (rebuilt 2022-10-28)

Last change 26 September 2022 (rebuilt 2022-11-01)