Performance benefit by using compiled model definitions in mkin

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")

##            gcc 
## "/usr/bin/gcc"

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

library("mkin")

## Loading required package: minpack.lm

## Loading required package: rootSolve

## Loading required package: inline

## Loading required package: methods

## Loading required package: parallel

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.

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")
}

## Loading required package: rbenchmark

##                    test replications elapsed relative user.self sys.self
## 3     deSolve, compiled            3   2.140    1.000     2.140        0
## 1 deSolve, not compiled            3  15.070    7.042    15.068        0
## 2      Eigenvalue based            3   2.577    1.204     2.576        0
##   user.child sys.child
## 3          0         0
## 1          0         0
## 2          0         0

We see that using the compiled model is by a factor of around 7 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")
}

## Successfully compiled differential equation model from auto-generated C code.

##                    test replications elapsed relative user.self sys.self
## 2     deSolve, compiled            3   3.646    1.000     3.644        0
## 1 deSolve, not compiled            3  30.752    8.434    30.752        0
##   user.child sys.child
## 2          0         0
## 1          0         0

Here we get a performance benefit of a factor of 8 using the version of the differential equation model compiled from C code!

This vignette was built with mkin 0.9.45.2 on

## R version 3.4.1 (2017-06-30)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 9 (stretch)

## CPU model: Intel(R) Core(TM) i7-4710MQ CPU @ 2.50GHz