compiled_models.RmdThis 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
## gcc
## "/usr/bin/gcc"First, we build a simple degradation model for a parent compound with one metabolite.
library("mkin", quietly = TRUE)
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 benchmark package.
if (require(rbenchmark)) {
b.1 <- benchmark(
"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),
replications = 3)
print(b.1)
factor_SFO_SFO <- round(b.1["1", "relative"])
} else {
factor_SFO_SFO <- NA
print("R package rbenchmark is not available")
}## Lade nötiges Paket: rbenchmark## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve",
## use_compiled = FALSE, : Observations with value of zero were removed from
## the data## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet =
## TRUE): Observations with value of zero were removed from the data## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet
## = TRUE): Observations with value of zero were removed from the data## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve",
## use_compiled = FALSE, : Observations with value of zero were removed from
## the data
## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve",
## use_compiled = FALSE, : Observations with value of zero were removed from
## the data
## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve",
## use_compiled = FALSE, : Observations with value of zero were removed from
## the data## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet =
## TRUE): Observations with value of zero were removed from the data
## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet =
## TRUE): Observations with value of zero were removed from the data
## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet =
## TRUE): Observations with value of zero were removed from the data## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet
## = TRUE): Observations with value of zero were removed from the data
## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet
## = TRUE): Observations with value of zero were removed from the data
## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet
## = TRUE): Observations with value of zero were removed from the data## test replications elapsed relative user.self sys.self
## 3 deSolve, compiled 3 3.067 1.000 3.065 0
## 1 deSolve, not compiled 3 28.135 9.173 28.122 0
## 2 Eigenvalue based 3 4.306 1.404 4.303 0
## user.child sys.child
## 3 0 0
## 1 0 0
## 2 0 0We see that using the compiled model is by a factor of around 9 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.
This evaluation is also taken from the example section of mkinfit.
if (require(rbenchmark)) {
FOMC_SFO <- mkinmod(
parent = mkinsub("FOMC", "m1"),
m1 = mkinsub( "SFO"))
b.2 <- benchmark(
"deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D,
use_compiled = FALSE, quiet = TRUE),
"deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
replications = 3)
print(b.2)
factor_FOMC_SFO <- round(b.2["1", "relative"])
} else {
factor_FOMC_SFO <- NA
print("R package benchmark is not available")
}## Successfully compiled differential equation model from auto-generated C code.## Warning in mkinfit(FOMC_SFO, FOCUS_2006_D, use_compiled = FALSE, quiet =
## TRUE): Observations with value of zero were removed from the data## Warning in mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE): Observations with
## value of zero were removed from the data## Warning in mkinfit(FOMC_SFO, FOCUS_2006_D, use_compiled = FALSE, quiet =
## TRUE): Observations with value of zero were removed from the data
## Warning in mkinfit(FOMC_SFO, FOCUS_2006_D, use_compiled = FALSE, quiet =
## TRUE): Observations with value of zero were removed from the data
## Warning in mkinfit(FOMC_SFO, FOCUS_2006_D, use_compiled = FALSE, quiet =
## TRUE): Observations with value of zero were removed from the data## Warning in mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE): Observations with
## value of zero were removed from the data
## Warning in mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE): Observations with
## value of zero were removed from the data
## Warning in mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE): Observations with
## value of zero were removed from the data## test replications elapsed relative user.self sys.self
## 2 deSolve, compiled 3 4.827 1.000 4.825 0
## 1 deSolve, not compiled 3 52.646 10.907 52.622 0
## user.child sys.child
## 2 0 0
## 1 0 0Here we get a performance benefit of a factor of 11 using the version of the differential equation model compiled from C code!
This vignette was built with mkin 0.9.49.4 on
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 9 (stretch)## CPU model: AMD Ryzen 7 1700 Eight-Core Processor