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")
## Loading required package: minpack.lm
## Loading required package: rootSolve
## Loading required package: inline
## Loading required package: methods
## Loading required package: parallel
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 4652.4576 4653.2263 4673.1220 4653.9950 4683.4543
## Eigenvalue based 749.7771 750.1327 764.7881 750.4882 772.2936
## deSolve, compiled 641.9152 647.6028 651.6533 653.2904 656.5223
## max neval cld
## 4712.9136 3 c
## 794.0990 3 b
## 659.7542 3 a
autoplot(mb.1)
We see that using the compiled model is by a factor of 7.1 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 7.123930
## Eigenvalue based 1.148782
## deSolve, compiled 1.000000
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 10.202245 10.243829 10.260112 10.285413 10.289045
## deSolve, compiled 1.176863 1.182699 1.188555 1.188536 1.194402
## max neval cld
## 10.292677 3 b
## 1.200267 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 8.7 using the version of the differential equation model compiled from C code using the inline package!
This vignette was built with mkin 0.9.39 on
## R Under development (unstable) (2016-03-23 r70368)
## 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