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: 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 4907.5349 4940.6725 4994.7965 4973.8101 5038.4272
## Eigenvalue based       782.8841  786.1423  798.1793  789.4005  805.8269
## deSolve, compiled      657.4028  659.1680  660.3935  660.9332  661.8889
##                             max neval cld
## deSolve, not compiled 5103.0444     3   b
## Eigenvalue based       822.2532     3  a 
## deSolve, compiled      662.8445     3  a

We see that using the compiled model is by a factor of 7.5 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 7.525435
## Eigenvalue based      1.194373
## 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 10.65938 10.675768 10.687261 10.69215 10.701200
## deSolve, compiled      1.16915  1.176235  1.187926  1.18332  1.197314
##                             max neval cld
## deSolve, not compiled 10.710246     3   b
## deSolve, compiled      1.211309     3  a
smb.2["median"]/smb.2["deSolve, compiled", "median"]
##                         median
## deSolve, not compiled 9.035727
## 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 0.9.41 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