From b74b5603abb50a3a09f41811dc5d79ceca5a3bc8 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 23 Jun 2015 13:16:47 +0200 Subject: Vignettes rebuilt by staticdocs::build_site() for static documentation on r-forge --- 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 2fbef8ae..a415c735 100644 --- a/vignettes/compiled_models.html +++ b/vignettes/compiled_models.html @@ -99,18 +99,18 @@ 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 6760.8015 6766.0464 6785.8509 6771.2913 6798.3756
-## Eigenvalue based       930.9980  944.5895  973.5743  958.1810  994.8625
-## deSolve, compiled      758.2721  811.0584  836.3348  863.8448  875.3661
+## deSolve, not compiled 6958.1752 7034.5639 7074.0173 7110.9526 7131.9383
+## Eigenvalue based       978.8821  988.5741 1012.6283  998.2660 1029.5014
+## deSolve, compiled      756.0280  767.9740  800.3639  779.9199  822.5318
 ##                             max neval
-## deSolve, not compiled 6825.4600     3
-## Eigenvalue based      1031.5439     3
-## deSolve, compiled      886.8875     3
-

We see that using the compiled model is by a factor of 7.8 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:

+## deSolve, not compiled 7152.9240 3 +## Eigenvalue based 1060.7367 3 +## deSolve, compiled 865.1437 3 +

We see that using the compiled model is by a factor of 9.1 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:

smb.1["median"]/smb.1["deSolve, compiled", "median"]
##                         median
-## deSolve, not compiled 7.838551
-## Eigenvalue based      1.109205
+## deSolve, not compiled 9.117542
+## Eigenvalue based      1.279960
 ## deSolve, compiled     1.000000
@@ -128,16 +128,16 @@ 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 14.231863 14.287058 14.336780 14.342252 14.389238
-## deSolve, compiled      1.344761  1.345452  1.361713  1.346142  1.370189
+## deSolve, not compiled 14.586587 14.604167 14.614147 14.621747 14.627927
+## deSolve, compiled      1.428573  1.449463  1.459828  1.470352  1.475455
 ##                             max neval
-## deSolve, not compiled 14.436224     3
-## deSolve, compiled      1.394235     3
+## deSolve, not compiled 14.634107 3 +## deSolve, compiled 1.480558 3 
smb.2["median"]/smb.2["deSolve, compiled", "median"]
##                         median
-## deSolve, not compiled 10.65434
-## deSolve, compiled      1.00000
-

Here we get a performance benefit of a factor of 10.7 using the version of the differential equation model compiled from C code using the inline package!

+## deSolve, not compiled 9.944383 +## deSolve, compiled 1.000000 +

Here we get a performance benefit of a factor of 9.9 using the version of the differential equation model compiled from C code using the inline package!

This vignette was built with mkin 0.9.37 on

## R version 3.2.1 (2015-06-18)
 ## Platform: x86_64-pc-linux-gnu (64-bit)
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cgit v1.2.1