Re-add the compiled models vignette

This was accidentally deleted in
438a889c37ffdf8f0c6585092da6abdb63b4575e on June 30!

1 files changed, 101 insertions, 0 deletions

diff --git a/vignettes/compiled_models.Rmd b/vignettes/compiled_models.Rmd
new file mode 100644
index 00000000..8dc74692
--- /dev/null
+++ b/vignettes/compiled_models.Rmd
@@ -0,0 +1,101 @@
+---

+title: "Performance benefit by using compiled model definitions in mkin"

+author: "Johannes Ranke"

+date: "`r Sys.Date()`"

+output:

+  html_document:

+    css: mkin_vignettes.css

+    toc: true

+    mathjax: null

+    theme: united

+vignette: >

+  %\VignetteIndexEntry{Performance benefit by using compiled model definitions in mkin}

+  %\VignetteEngine{knitr::rmarkdown}

+  \usepackage[utf8]{inputenc}

+---

+

+```{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

+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 microbenchmark package.

+

+

+```{r benchmark_SFO_SFO}

+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)

+```

+

+We see that using the compiled model is by a factor of 

+`r round(smb.1["deSolve, not compiled", "median"]/smb.1["deSolve, compiled", "median"], 1)` 

+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["deSolve, 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 = mkinsub("FOMC", "m1"),

+  m1 = mkinsub( "SFO"))

+

+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)

+smb.2["median"]/smb.2["deSolve, compiled", "median"]

+```

+

+Here we get a performance benefit of a factor of 

+`r round(smb.2["deSolve, not compiled", "median"]/smb.2["deSolve, compiled", "median"], 1)` 

+using the version of the differential equation model compiled from C code using

+the inline package!

+

+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]]))

+}

+```