---
title: "Performance benefit by using compiled model definitions in mkin"
output:
  html_document:
    css: mkin_vignettes.css
    toc: true
    mathjax: null
    theme: united
---
<!--
%\VignetteEngine{knitr::rmarkdown}
%\VignetteIndexEntry{Performance benefit by using compiled model definitions in mkin}
-->

```{r, include = FALSE}
library(knitr)
opts_chunk$set(tidy = FALSE, cache = TRUE)
```

# Benchmark for a model that can also be solved with Eigenvalues

This evaluation is taken from the example section of mkinfit. When using an mkin version
greater or equal than 0.9-36 and the C++ compiler g++ is installed and functional (on Windows,
install Rtools), you will get a message that the model is being compiled when
defining a model using mkinmod.

```{r create_SFO_SFO}
library("mkin")
SFO_SFO <- mkinmod(
  parent = list(type = "SFO", to = "m1", sink = TRUE),
  m1 = list(type = "SFO"), odeintr_compile = "yes")
```

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.


```{r benchmark_SFO_SFO, echo=-(1:2)}
library("microbenchmark")
mb.1 <- microbenchmark(
  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet = TRUE),
  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet = TRUE),
  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "odeintr", quiet = TRUE),
  times = 3, control = list(warmup = 1))
smb.1 <- summary(mb.1)[-1]
rownames(smb.1) <- c("deSolve, not compiled", "Eigenvalue based", "odeintr, compiled")
print(smb.1)
```

We see that using the compiled model is more than a factor of 7 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:

```{r}
smb.1["median"]/smb.1["odeintr, compiled", "median"]
```

# Benchmark for a model that can not be solved with Eigenvalues

This evaluation is also taken from the example section of mkinfit. 

```{r benchmark_FOMC_SFO}
FOMC_SFO <- mkinmod(
  parent = list(type = "FOMC", to = "m1", sink = TRUE),
  m1 = list(type = "SFO"))

mb.2 <- microbenchmark(
  mkinfit(FOMC_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet = TRUE),
  mkinfit(FOMC_SFO, FOCUS_2006_D, solution_type = "odeintr", quiet = TRUE),
  times = 3, control = list(warmup = 1))
smb.2 <- summary(mb.2)[-1]
rownames(smb.2) <- c("deSolve, not compiled", "odeintr, compiled")
print(smb.2)
smb.2["median"]/smb.2["odeintr, compiled", "median"]

```

Here we get a performance benefit of more than a factor of 8 using the version
of the differential equation model compiled from C++ code using the odeintr package!