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")
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")
library("ggplot2")
mb.1 <- microbenchmark(
  "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),
  times = 3, control = list(warmup = 0))
## Warning in microbenchmark(`deSolve, not compiled` = mkinfit(SFO_SFO,
## FOCUS_2006_D, : Could not measure overhead. Your clock might lack
## precision.
smb.1 <- summary(mb.1)
print(mb.1)
## Unit: seconds
##                   expr       min        lq      mean    median        uq
##  deSolve, not compiled 11.288546 11.350575 11.415449 11.412604 11.478900
##       Eigenvalue based  1.688726  1.691521  1.821009  1.694315  1.887151
##      deSolve, compiled  1.454977  1.469034  1.473792  1.483092  1.483199
##        max neval cld
##  11.545196     3   b
##   2.079986     3  a 
##   1.483307     3  a
autoplot(mb.1)

We see that using the compiled model is by a factor of 7.7 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:

rownames(smb.1) <- smb.1$expr
smb.1["median"]/smb.1["deSolve, compiled", "median"]
##                         median
## deSolve, not compiled 7.695144
## Eigenvalue based      1.142421
## 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(
  "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, 
                                    use_compiled = FALSE, quiet = TRUE),
  "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
  times = 3, control = list(warmup = 0))
## Warning in microbenchmark(`deSolve, not compiled` = mkinfit(FOMC_SFO,
## FOCUS_2006_D, : Could not measure overhead. Your clock might lack
## precision.
smb.2 <- summary(mb.2)
print(mb.2)
## Unit: seconds
##                   expr       min       lq      mean    median        uq
##  deSolve, not compiled 23.078491 23.27986 23.445198 23.481237 23.628551
##      deSolve, compiled  2.355316  2.37580  2.415102  2.396284  2.444995
##        max neval cld
##  23.775865     3   b
##   2.493705     3  a
smb.2["median"]/smb.2["deSolve, compiled", "median"]
##   median
## 1     NA
## 2     NA
autoplot(mb.2)

Here we get a performance benefit of a factor of 9.8 using the version of the differential equation model compiled from C code using the inline package!

This vignette was built with mkin 0.9.42 on

## R version 3.2.4 Patched (2016-03-28 r70390)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 8 (jessie)
## CPU model: Intel(R) Xeon(R) CPU           X5650  @ 2.67GHz