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 6559.0772 6574.0500 6642.0659 6589.0229 6683.5603
##       Eigenvalue based  921.0723  931.1284  940.3973  941.1845  950.0598
##      deSolve, compiled  736.6534  741.6706  756.3600  746.6878  766.2132
##        max neval cld
##  6778.0978     3   c
##   958.9351     3  b 
##   785.7386     3 a

autoplot(mb.1)

We see that using the compiled model is by a factor of 8.8 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:

rownames(smb.1) <- smb.1$expr
smb.1["median"]/smb.1["deSolve, compiled", "median"]

##                         median
## deSolve, not compiled 8.824334
## Eigenvalue based      1.260479
## 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 13.587184 13.616178 13.673277 13.64517 13.716324
##      deSolve, compiled  1.307508  1.317114  1.337594  1.32672  1.352637
##        max neval cld
##  13.787476     3   b
##   1.378553     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 10.3 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