From 6b7c2049d4feb9dd76dd532830adba23b8a5007f Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 28 Sep 2016 08:50:17 +0200 Subject: Add drat target and rebuild for drat --- vignettes/compiled_models.html | 332 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 332 insertions(+) create mode 100644 vignettes/compiled_models.html (limited to 'vignettes/compiled_models.html') diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html new file mode 100644 index 00000000..7a9537c7 --- /dev/null +++ b/vignettes/compiled_models.html @@ -0,0 +1,332 @@ + + + + + + + + + + + + + + + +Performance benefit by using compiled model definitions in mkin + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

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

+
Sys.which("gcc")
+
##            gcc 
+## "/usr/bin/gcc"
+

First, we build a simple degradation model for a parent compound with one metabolite.

+
library("mkin")
+
## Loading required package: minpack.lm
+
## Loading required package: rootSolve
+
## Loading required package: inline
+
## Loading required package: methods
+
## Loading required package: parallel
+
SFO_SFO <- mkinmod(
+  parent = mkinsub("SFO", "m1"),
+  m1 = mkinsub("SFO"))
+
## Successfully compiled 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")
+library("ggplot2")
+mb.1 <- microbenchmark(
+  "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),
+  times = 3, control = list(warmup = 0))
+
## Warning in microbenchmark(`deSolve, not compiled` = mkinfit(SFO_SFO,
+## FOCUS_2006_D, : Could not measure overhead. Your clock might lack
+## precision.
+
smb.1 <- summary(mb.1)
+print(mb.1)
+
## Unit: milliseconds
+##                   expr       min        lq      mean    median        uq
+##  deSolve, not compiled 5191.7067 5234.0766 5254.1638 5276.4465 5285.3923
+##       Eigenvalue based  837.7118  839.5009  848.7273  841.2900  854.2351
+##      deSolve, compiled  725.9412  738.4609  776.5436  750.9807  801.8448
+##        max neval cld
+##  5294.3381     3   b
+##   867.1802     3  a 
+##   852.7088     3  a
+
autoplot(mb.1)
+

+

We see that using the compiled model is by 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:

+
rownames(smb.1) <- smb.1$expr
+smb.1["median"]/smb.1["deSolve, compiled", "median"]
+
##                         median
+## deSolve, not compiled 7.026075
+## Eigenvalue based      1.120255
+## 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 = mkinsub("FOMC", "m1"),
+  m1 = mkinsub( "SFO"))
+
## Successfully compiled differential equation model from auto-generated C code.
+
mb.2 <- microbenchmark(
+  "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, 
+                                    use_compiled = FALSE, quiet = TRUE),
+  "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
+  times = 3, control = list(warmup = 0))
+
## Warning in microbenchmark(`deSolve, not compiled` = mkinfit(FOMC_SFO,
+## FOCUS_2006_D, : Could not measure overhead. Your clock might lack
+## precision.
+
smb.2 <- summary(mb.2)
+print(mb.2)
+
## Unit: seconds
+##                   expr       min        lq      mean   median        uq
+##  deSolve, not compiled 10.977718 11.003379 11.044286 11.02904 11.077570
+##      deSolve, compiled  1.289028  1.296324  1.332644  1.30362  1.354451
+##        max neval cld
+##  11.126100     3   b
+##   1.405282     3  a
+
smb.2["median"]/smb.2["deSolve, compiled", "median"]
+
##   median
+## 1     NA
+## 2     NA
+
autoplot(mb.2)
+

+

Here we get a performance benefit of a factor of 8.5 using the version of the differential equation model compiled from C code!

+

This vignette was built with mkin 0.9.44.9000 on

+
## R version 3.3.1 (2016-06-21)
+## Platform: x86_64-pc-linux-gnu (64-bit)
+## Running under: Debian GNU/Linux 8 (jessie)
+
## CPU model: Intel(R) Core(TM) i7-4710MQ CPU @ 2.50GHz
+
+ + + +
+
+ +
+ + + + + + -- cgit v1.2.1