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---
title: "Performance benefit by using compiled model definitions in mkin"
author: "Johannes Ranke"
date: "`r Sys.Date()`"
output:
html_document:
theme: united
toc: true
toc_float: true
mathjax: null
vignette: >
%\VignetteIndexEntry{Performance benefit by using compiled model definitions in mkin}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
library(knitr)
opts_chunk$set(tidy = FALSE, cache = FALSE)
```
## 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
```{r check_gcc}
Sys.which("gcc")
```
First, we build a simple degradation model for a parent compound with one metabolite.
```{r create_SFO_SFO}
library("mkin")
SFO_SFO <- mkinmod(
parent = mkinsub("SFO", "m1"),
m1 = mkinsub("SFO"))
```
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.
```{r benchmark_SFO_SFO, fig.height = 3}
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 benchmark is not available")
}
```
We see that using the compiled model is by a factor of around
`r factor_SFO_SFO`
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.
## Model that can not be solved with Eigenvalues
This evaluation is also taken from the example section of mkinfit.
```{r benchmark_FOMC_SFO, fig.height = 3}
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")
}
```
Here we get a performance benefit of a factor of
`r factor_FOMC_SFO`
using the version of the differential equation model compiled from C code!
This vignette was built with mkin `r packageVersion("mkin")` on
```{r sessionInfo, echo = FALSE}
cat(capture.output(sessionInfo())[1:3], sep = "\n")
if(!inherits(try(cpuinfo <- readLines("/proc/cpuinfo")), "try-error")) {
cat(gsub("model name\t: ", "CPU model: ", cpuinfo[grep("model name", cpuinfo)[1]]))
}
```
|
