Benchmark for a 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 microbenchmark package.

library("microbenchmark")
mb.1 <- microbenchmark(
  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", use_compiled = FALSE, 
          quiet = TRUE),
  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet = TRUE),
  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet = TRUE),
  times = 3, control = list(warmup = 1))
smb.1 <- summary(mb.1)[-1]
rownames(smb.1) <- c("deSolve, not compiled", "Eigenvalue based", "deSolve, compiled")
print(smb.1)
##                             min        lq      mean    median        uq
## deSolve, not compiled 4920.5498 4957.8305 5073.8005 4995.1112 5150.4259
## Eigenvalue based       792.7603  820.6244  849.2773  848.4885  877.5358
## deSolve, compiled      663.5431  673.4949  678.4844  683.4468  685.9551
##                             max neval cld
## deSolve, not compiled 5305.7406     3   b
## Eigenvalue based       906.5832     3  a 
## deSolve, compiled      688.4634     3  a

We see that using the compiled model is by a factor of 7.3 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:

smb.1["median"]/smb.1["deSolve, compiled", "median"]
##                         median
## deSolve, not compiled 7.308706
## Eigenvalue based      1.241484
## deSolve, compiled     1.000000

Benchmark for a model that can not be solved with Eigenvalues

This evaluation is also taken from the example section of mkinfit.

FOMC_SFO <- mkinmod(
  parent = mkinsub("FOMC", "m1"),
  m1 = mkinsub( "SFO"))
## Successfully compiled differential equation model from auto-generated C code.
mb.2 <- microbenchmark(
  mkinfit(FOMC_SFO, FOCUS_2006_D, use_compiled = FALSE, quiet = TRUE),
  mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
  times = 3, control = list(warmup = 1))
smb.2 <- summary(mb.2)[-1]
rownames(smb.2) <- c("deSolve, not compiled", "deSolve, compiled")
print(smb.2)
##                             min        lq      mean    median        uq
## deSolve, not compiled 10.710141 10.757988 10.810178 10.805835 10.860196
## deSolve, compiled      1.200581  1.203966  1.211877  1.207351  1.217525
##                             max neval cld
## deSolve, not compiled 10.914558     3   b
## deSolve, compiled      1.227699     3  a
smb.2["median"]/smb.2["deSolve, compiled", "median"]
##                         median
## deSolve, not compiled 8.950036
## deSolve, compiled     1.000000

Here we get a performance benefit of a factor of 9 using the version of the differential equation model compiled from C code using the inline package!

This vignette was built with mkin 0.9.40.900 on

## R version 3.2.2 (2015-08-14)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 8 (jessie)
## CPU model: Intel(R) Core(TM) i7-4710MQ CPU @ 2.50GHz