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")
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 6760.8015 6766.0464 6785.8509 6771.2913 6798.3756
## Eigenvalue based 930.9980 944.5895 973.5743 958.1810 994.8625
## deSolve, compiled 758.2721 811.0584 836.3348 863.8448 875.3661
## max neval
## deSolve, not compiled 6825.4600 3
## Eigenvalue based 1031.5439 3
## deSolve, compiled 886.8875 3
We see that using the compiled model is by a factor of 7.8 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 7.838551
## Eigenvalue based 1.109205
## 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(
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 14.231863 14.287058 14.336780 14.342252 14.389238
## deSolve, compiled 1.344761 1.345452 1.361713 1.346142 1.370189
## max neval
## deSolve, not compiled 14.436224 3
## deSolve, compiled 1.394235 3
smb.2["median"]/smb.2["deSolve, compiled", "median"]
## median
## deSolve, not compiled 10.65434
## deSolve, compiled 1.00000
Here we get a performance benefit of a factor of 10.7 using the version of the differential equation model compiled from C code using the inline package!
This vignette was built with mkin 0.9.37 on
## R version 3.2.1 (2015-06-18)
## 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