---
title: "Performance benefit by using compiled model definitions in mkin"
author: "Johannes Ranke"
output: rmarkdown::html_vignette
date: "`r Sys.Date()`"
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]]))
}
```
