From 7faf98ac5475bb2041d7e434478c58c2f2cec0fd Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 28 Jun 2016 08:23:38 +0200 Subject: Static documentation rebuilt by staticdocs::build_site() --- inst/web/vignettes/compiled_models.html | 38 ++++++++++++++++----------------- 1 file changed, 19 insertions(+), 19 deletions(-) (limited to 'inst/web/vignettes/compiled_models.html') diff --git a/inst/web/vignettes/compiled_models.html b/inst/web/vignettes/compiled_models.html index 5e426a7f..cec76ef9 100644 --- a/inst/web/vignettes/compiled_models.html +++ b/inst/web/vignettes/compiled_models.html @@ -20,7 +20,7 @@ - + @@ -250,21 +250,21 @@ mb.1 <- microbenchmark( print(mb.1) 
## Unit: seconds
 ##                   expr       min        lq      mean    median        uq
-##  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
-##  26.245391     3   b
-##   2.302308     3  a 
-##   1.907281     3  a
+## deSolve, not compiled 25.422123 25.889685 26.065978 26.357247 26.387905 +## Eigenvalue based 2.243667 2.254539 2.277770 2.265412 2.294821 +## deSolve, compiled 1.849468 1.865343 1.871339 1.881219 1.882274 +## max neval cld +## 26.41856 3 b +## 2.32423 3 a +## 1.88333 3 a 
autoplot(mb.1)
-

-

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:

+

+

We see that using the compiled model is by a factor of 14 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 13.297425
-## Eigenvalue based       1.208117
+## deSolve, not compiled 14.010730
+## Eigenvalue based       1.204226
 ## deSolve, compiled      1.000000
@@ -285,20 +285,20 @@ 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 53.69252 53.938844 54.137601 54.185167 54.360141
-##      deSolve, compiled  3.42508  3.526298  3.588392  3.627516  3.670048
+##                   expr       min       lq      mean    median        uq
+##  deSolve, not compiled 54.386189 54.39423 54.477986 54.402271 54.523884
+##      deSolve, compiled  3.424205  3.53522  3.574587  3.646236  3.649778
 ##        max neval cld
-##  54.535116     3   b
-##   3.712579     3  a
+## 54.645498 3 b +## 3.653319 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

+

This vignette was built with mkin 0.9.43.9000 on

## R version 3.3.1 (2016-06-21)
 ## Platform: x86_64-pc-linux-gnu (64-bit)
 ## Running under: Debian GNU/Linux 8 (jessie)
-- cgit v1.2.1