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 compiler (gcc) is installed, you will see a message that the model is being compiled from autogenerated C code when defining a model using mkinmod. The package tests 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")
SFO_SFO <- mkinmod(
  parent = mkinsub("SFO", "m1"),
  m1 = mkinsub("SFO"))
## Compiling 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 6737.589 6818.2149 6911.3916 6898.8407 6998.2929
## Eigenvalue based       945.433  968.8592  979.7477  992.2854  996.9051
## deSolve, compiled      744.785  748.8107  770.7521  752.8364  783.7357
##                            max neval
## deSolve, not compiled 7097.745     3
## Eigenvalue based      1001.525     3
## deSolve, compiled      814.635     3

We see that using the compiled model is by a factor of 9.2 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 9.163798
## Eigenvalue based      1.318062
## 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"))
## Compiling 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 13.955273 13.961009 14.041563 13.966745 14.084708
## deSolve, compiled      1.350567  1.371225  1.381397  1.391882  1.396812
##                             max neval
## deSolve, not compiled 14.202672     3
## deSolve, compiled      1.401743     3
smb.2["median"]/smb.2["deSolve, compiled", "median"]
##                         median
## deSolve, not compiled 10.03443
## deSolve, compiled      1.00000

Here we get a performance benefit of a factor of 10 using the version of the differential equation model compiled from C code using the inline package!

This vignette was built with mkin 0.9.37 on

## R version 3.2.1 (2015-06-18)
## 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