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authorJohannes Ranke <jranke@uni-bremen.de>2015-11-09 08:52:01 +0100
committerJohannes Ranke <jranke@uni-bremen.de>2015-11-09 08:52:01 +0100
commit415ca2bea5d5c3815bd9f8fa1566cec5bb3fc775 (patch)
tree3d87c29a6ead75422a05948607f60d56592e2452 /vignettes
parent84ba6145b0962472f5b23dc7c3fc01cd09acdaa8 (diff)
Re-add the compiled models vignette
This was accidentally deleted in 438a889c37ffdf8f0c6585092da6abdb63b4575e on June 30!
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+---
+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]]))
+}
+```

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