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.

library("mkin")
SFO_SFO <- mkinmod(
  parent = list(type = "SFO", to = "m1", sink = TRUE),
  m1 = list(type = "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 4969.585 5033.7311 5092.7389 5097.8773 5154.3160
## Eigenvalue based       868.731  891.7239  909.6449  914.7169  930.1018
## deSolve, compiled     4935.049 4935.4796 4968.2150 4935.9097 4984.7978
##                             max neval
## deSolve, not compiled 5210.7547     3
## Eigenvalue based       945.4867     3
## deSolve, compiled     5033.6858     3

We see that using the compiled model is almost a factor of 8 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 1.0328141
## Eigenvalue based      0.1853188
## deSolve, compiled     1.0000000

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 = list(type = "FOMC", to = "m1", sink = TRUE),
  m1 = list(type = "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 11.745276 11.754288 11.820726 11.763300 11.858451
## deSolve, compiled      1.385829  1.386407  1.400841  1.386985  1.408347
##                            max neval
## deSolve, not compiled 11.95360     3
## deSolve, compiled      1.42971     3
smb.2["median"]/smb.2["deSolve, compiled", "median"]
##                       median
## deSolve, not compiled 8.4812
## deSolve, compiled     1.0000

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