From e3fa9f6d27369abcbbc6045a9a7b40c2b17c122e Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Fri, 26 Jun 2015 16:38:15 +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 a62f3826..a8f6e3ef 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 7047.6039 7083.3201 7123.5962 7119.0364 7161.5924
-## Eigenvalue based       901.5593  924.3357  968.8689  947.1121 1002.5238
-## deSolve, compiled      765.7604  770.7657  786.8638  775.7709  797.4156
+## deSolve, not compiled 6767.3728 6834.5128 6879.5969 6901.6528 6935.7090
+## Eigenvalue based       977.5545 1161.8591 1225.1262 1346.1637 1348.9120
+## deSolve, compiled      761.0689  761.8958  772.3379  762.7228  777.9724
 ##                             max neval
-## deSolve, not compiled 7204.1483     3
-## Eigenvalue based      1057.9355     3
-## deSolve, compiled      819.0602     3
-

We see that using the compiled model is by a factor of 9.2 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 6969.7653 3 +## Eigenvalue based 1351.6603 3 +## deSolve, compiled 793.2221 3 +

We see that using the compiled model is by a factor of 9 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 9.176725
-## Eigenvalue based      1.220866
+## deSolve, not compiled 9.048704
+## Eigenvalue based      1.764945
 ## 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 14.080456 14.209689 14.287313 14.338922 14.390742
-## deSolve, compiled      1.467266  1.521451  1.555168  1.575636  1.599119
+## deSolve, not compiled 14.161917 14.189080 14.201763 14.216243 14.221687
+## deSolve, compiled      1.358691  1.366613  1.393306  1.374535  1.410614
 ##                             max neval
-## deSolve, not compiled 14.442561     3
-## deSolve, compiled      1.622601     3
+## deSolve, not compiled 14.227131 3 +## deSolve, compiled 1.446693 3 
smb.2["median"]/smb.2["deSolve, compiled", "median"]
##                         median
-## deSolve, not compiled 9.100402
-## deSolve, compiled     1.000000
-

Here we get a performance benefit of a factor of 9.1 using the version of the differential equation model compiled from C code using the inline package!

+## deSolve, not compiled 10.34258 +## deSolve, compiled 1.00000 +

Here we get a performance benefit of a factor of 10.3 using the version of the differential equation model compiled from C code using the inline package!

This vignette was built with mkin 0.9.39 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