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 9442.5119 9447.2060 9458.3420 9451.9001 9466.2571
## Eigenvalue based 868.6312 872.4552 895.3422 876.2792 908.6977
## deSolve, compiled 691.9663 697.5653 701.1004 703.1643 705.6674
## max neval cld
## deSolve, not compiled 9480.6141 3 c
## Eigenvalue based 941.1163 3 b
## deSolve, compiled 708.1706 3 a
We see that using the compiled model is by a factor of 13.4 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 13.441951
## Eigenvalue based 1.246194
## 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 20.444632 20.48824 20.557595 20.531857 20.614077
## deSolve, compiled 1.251733 1.25179 1.275227 1.251846 1.286973
## max neval cld
## deSolve, not compiled 20.6963 3 b
## deSolve, compiled 1.3221 3 a
smb.2["median"]/smb.2["deSolve, compiled", "median"]
## median
## deSolve, not compiled 16.40126
## deSolve, compiled 1.00000
Here we get a performance benefit of a factor of 16.4 using the version of the differential equation model compiled from C code using the inline package!
This vignette was built with mkin 0.9.41 on
## R version 3.2.2 (2015-08-14)
## 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