From 1b2bdb8c02b1090f6c20f3ce9f7b461aab26e21c Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 28 Jun 2016 05:32:21 +0200 Subject: Static documentation rebuilt by staticdocs::build_site() --- vignettes/compiled_models.html | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) (limited to 'vignettes/compiled_models.html') diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html index 9e2dff36..5e426a7f 100644 --- a/vignettes/compiled_models.html +++ b/vignettes/compiled_models.html @@ -250,21 +250,21 @@ mb.1 <- microbenchmark( print(mb.1) 
## Unit: seconds
 ##                   expr       min        lq      mean    median        uq
-##  deSolve, not compiled 25.160343 25.384579 25.502052 25.608814 25.672906
-##       Eigenvalue based  2.219737  2.234679  2.244043  2.249621  2.256196
-##      deSolve, compiled  1.825299  1.843813  1.856078  1.862327  1.871467
+##  deSolve, not compiled 25.042204 25.078629 25.467550 25.115054 25.680223
+##       Eigenvalue based  2.273059  2.277424  2.285719  2.281790  2.292049
+##      deSolve, compiled  1.878785  1.883750  1.891594  1.888716  1.897998
 ##        max neval cld
-##  25.736998     3   b
-##   2.262771     3  a 
-##   1.880606     3  a
+## 26.245391 3 b +## 2.302308 3 a +## 1.907281 3 a 
autoplot(mb.1)
-

-

We see that using the compiled model is by a factor of 13.8 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:

+

+

We see that using the compiled model is by a factor of 13.3 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 13.750973
-## Eigenvalue based       1.207962
+## deSolve, not compiled 13.297425
+## Eigenvalue based       1.208117
 ## deSolve, compiled      1.000000
@@ -285,18 +285,18 @@ smb.1["median"]/smb.1["deSolve, compiled", "median" 
smb.2 <- summary(mb.2)
 print(mb.2)
## Unit: seconds
-##                   expr       min        lq      mean   median        uq
-##  deSolve, not compiled 54.725198 54.787875 54.893809 54.85055 54.978114
-##      deSolve, compiled  3.618315  3.644838  3.670582  3.67136  3.696716
+##                   expr      min        lq      mean    median        uq
+##  deSolve, not compiled 53.69252 53.938844 54.137601 54.185167 54.360141
+##      deSolve, compiled  3.42508  3.526298  3.588392  3.627516  3.670048
 ##        max neval cld
-##  55.105678     3   b
-##   3.722071     3  a
+## 54.535116 3 b +## 3.712579 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 14.9 using the version of the differential equation model compiled from C code!

This vignette was built with mkin 0.9.43 on

## R version 3.3.1 (2016-06-21)
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cgit v1.2.1