Performance benefit by using compiled model definitions in mkin"

library(knitr)
opts_chunk$set(tidy = FALSE, cache = FALSE)

Model that can also be solved with Eigenvalues

This evaluation is taken from the example section of mkinfit. When using an mkin version equal to or greater than 0.9-36 and a C compiler (gcc) is available, you will see a message that the model is being compiled from autogenerated C code when defining a model using mkinmod. The mkinmod() function checks for presence of the gcc compiler using

Sys.which("gcc")

First, we build a simple degradation model for a parent compound with one metabolite.

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

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.

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 benchmark is not available")
}

We see that using the compiled model is by a factor of around r factor_SFO_SFO 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.

Model that can not be solved with Eigenvalues

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")
}

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

This vignette was built with mkin r packageVersion("mkin") on

cat(capture.output(sessionInfo())[1:3], sep = "\n")
if(!inherits(try(cpuinfo <- readLines("/proc/cpuinfo")), "try-error")) {
  cat(gsub("model name\t: ", "CPU model: ", cpuinfo[grep("model name", cpuinfo)[1]]))
}


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mkin documentation built on Nov. 17, 2017, 4:02 a.m.