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: milliseconds
##                   expr       min        lq      mean    median        uq
##  deSolve, not compiled 6581.1731 6598.7831 6630.0395 6616.3931 6654.4726
##       Eigenvalue based  880.1617  890.3517  911.2758  900.5416  926.8328
##      deSolve, compiled  739.4343  749.6078  753.6543  759.7813  760.7643
##        max neval cld
##  6692.5522     3   c
##   953.1240     3  b 
##   761.7474     3 a

autoplot(mb.1)

We see that using the compiled model is by a factor of 8.7 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 8.708286
## Eigenvalue based      1.185264
## deSolve, compiled     1.000000

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 13.756286 13.813205 13.874083 13.870125 13.932982
##      deSolve, compiled  1.323196  1.372916  1.391915  1.422635  1.426274
##        max neval cld
##  13.995838     3   b
##   1.429913     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 9.7 using the version of the differential equation model compiled from C code!

This vignette was built with mkin 0.9.44.9000 on

## R version 3.3.2 (2016-10-31)
## 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