From 0b98c459c30a0629a728acf6b311de035c55fb64 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 18 Jul 2018 15:18:30 +0200 Subject: Correct references to the Rocke and Lorenzato model Rename 'sigma_rl' to 'sigma_twocomp' as the Rocke and Lorenzato model assumes lognormal distribution for large y. Rebuild static documentation. --- vignettes/compiled_models.html | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) (limited to 'vignettes/compiled_models.html') diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html index d8c5b19b..81bff548 100644 --- a/vignettes/compiled_models.html +++ b/vignettes/compiled_models.html @@ -12,7 +12,7 @@ - + Performance benefit by using compiled model definitions in mkin @@ -70,7 +70,7 @@ code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Inf

Performance benefit by using compiled model definitions in mkin

Johannes Ranke

-

2018-03-09

+

2018-07-17

@@ -105,14 +105,14 @@ SFO_SFO <- mkinmod( }
## Loading required package: rbenchmark
##                    test replications elapsed relative user.self sys.self
-## 3     deSolve, compiled            3   1.980    1.000     1.979        0
-## 1 deSolve, not compiled            3  13.926    7.033    13.914        0
-## 2      Eigenvalue based            3   2.362    1.193     2.360        0
+## 3     deSolve, compiled            3   2.116    1.000     2.115        0
+## 1 deSolve, not compiled            3  16.563    7.828    16.555        0
+## 2      Eigenvalue based            3   2.599    1.228     2.597        0
 ##   user.child sys.child
 ## 3          0         0
 ## 1          0         0
 ## 2          0         0
-

We see that using the compiled model is by a factor of around 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.

+

We see that using the compiled model is by a factor of around 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.

Model that can not be solved with Eigenvalues

@@ -135,14 +135,14 @@ SFO_SFO <- mkinmod( }
## Successfully compiled differential equation model from auto-generated C code.
##                    test replications elapsed relative user.self sys.self
-## 2     deSolve, compiled            3   3.437    1.000     3.433        0
-## 1 deSolve, not compiled            3  30.406    8.847    30.380        0
+## 2     deSolve, compiled            3   3.809    1.000     3.806        0
+## 1 deSolve, not compiled            3  35.885    9.421    35.866        0
 ##   user.child sys.child
 ## 2          0         0
 ## 1          0         0

Here we get a performance benefit of a factor of 9 using the version of the differential equation model compiled from C code!

-

This vignette was built with mkin 0.9.46.3 on

-
## R version 3.4.3 (2017-11-30)
+

This vignette was built with mkin 0.9.47.1 on

+
## R version 3.5.1 (2018-07-02)
 ## Platform: x86_64-pc-linux-gnu (64-bit)
 ## Running under: Debian GNU/Linux 9 (stretch)
## CPU model: AMD Ryzen 7 1700 Eight-Core Processor
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