From a26b44d15c11ebb41083fc2efab0cc91a027b55b Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sat, 18 Apr 2015 00:06:38 +0200 Subject: Add a vignette showing the performance of compiled models --- vignettes/compiled_models.Rmd | 87 +++++++++++++++++++++++ vignettes/compiled_models.html | 153 +++++++++++++++++++++++++++++++++++++++++ vignettes/mkin_vignettes.css | 16 +++++ 3 files changed, 256 insertions(+) create mode 100644 vignettes/compiled_models.Rmd create mode 100644 vignettes/compiled_models.html create mode 100644 vignettes/mkin_vignettes.css (limited to 'vignettes') diff --git a/vignettes/compiled_models.Rmd b/vignettes/compiled_models.Rmd new file mode 100644 index 00000000..bac284c5 --- /dev/null +++ b/vignettes/compiled_models.Rmd @@ -0,0 +1,87 @@ +--- +title: "Performance benefit by using compiled model definitions in mkin" +output: + html_document: + css: mkin_vignettes.css + toc: true + mathjax: null + theme: united +--- + + +```{r, include = FALSE} +library(knitr) +opts_chunk$set(tidy = FALSE, cache = TRUE) +if (!require("ccSolve")) + message("Please install the ccSolve package for this vignette to produce sensible output") + +``` + +# 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 than 0.9-36 and the ccSolve package is installed and functional, 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")) +``` + +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)} +# Redefining the model, in order not to confuse the knitr cache which leads to segfaults +suppressMessages(SFO_SFO <- mkinmod( + parent = list(type = "SFO", to = "m1", sink = TRUE), + m1 = list(type = "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 almost a factor of 8 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 = list(type = "FOMC", to = "m1", sink = TRUE), + m1 = list(type = "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 more than a factor of 8 using the version +of the differential equation model compiled from C code using the ccSolve package! diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html new file mode 100644 index 00000000..2f2a6edb --- /dev/null +++ b/vignettes/compiled_models.html @@ -0,0 +1,153 @@ + + + + + + + + + + + + +Performance benefit by using compiled model definitions in mkin + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+

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 than 0.9-36 and the ccSolve package is installed and functional, you will get a message that the model is being compiled when defining a model using mkinmod.

+
library("mkin")
+SFO_SFO <- mkinmod(
+  parent = list(type = "SFO", to = "m1", sink = TRUE),
+  m1 = list(type = "SFO"))
+
## Compiling differential equation model from auto-generated C code...
+

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.

+
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)
+
##                             min        lq      mean    median        uq
+## deSolve, not compiled 6192.0125 6195.3470 6211.0309 6198.6816 6220.5401
+## Eigenvalue based       956.7604 1008.7224 1026.2572 1060.6844 1061.0055
+## deSolve, compiled      869.6880  871.9315  883.4929  874.1751  890.3953
+##                             max neval
+## deSolve, not compiled 6242.3986     3
+## Eigenvalue based      1061.3266     3
+## deSolve, compiled      906.6155     3
+

We see that using the compiled model is almost a factor of 8 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:

+
smb.1["median"]/smb.1["deSolve, compiled", "median"]
+
##                         median
+## deSolve, not compiled 7.120877
+## Eigenvalue based      1.205328
+## deSolve, compiled     1.000000
+
+
+

Benchmark for a model that can not be solved with Eigenvalues

+

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

+
FOMC_SFO <- mkinmod(
+  parent = list(type = "FOMC", to = "m1", sink = TRUE),
+  m1 = list(type = "SFO"))
+
## Compiling differential equation model from auto-generated C code...
+
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)
+
##                             min        lq      mean    median        uq
+## deSolve, not compiled 13.297283 13.427702 13.481155 13.558121 13.573092
+## deSolve, compiled      1.486926  1.526887  1.546851  1.566848  1.576813
+##                             max neval
+## deSolve, not compiled 13.588063     3
+## deSolve, compiled      1.586778     3
+
smb.2["median"]/smb.2["deSolve, compiled", "median"]
+
##                         median
+## deSolve, not compiled 8.653119
+## deSolve, compiled     1.000000
+

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 ccSolve package!

+
+ + +
+ + + + + + diff --git a/vignettes/mkin_vignettes.css b/vignettes/mkin_vignettes.css new file mode 100644 index 00000000..3bd91ab1 --- /dev/null +++ b/vignettes/mkin_vignettes.css @@ -0,0 +1,16 @@ +/* Thanks to Steve Powell for http://rpubs.com/stevepowell99/floating-css */ +#TOC { + position: fixed; + left: 0; + top: 0; + width: 200px; + height: 100%; + overflow:auto; +} + +body { + max-width: 800px; + margin: auto; + margin-left:210px; + line-height: 20px; +} -- cgit v1.2.1