From 01d9de6ff165c64ffc4366f2eeb3d2649b5c74c0 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 22 Jun 2015 06:09:00 +0200 Subject: Version bump, correct benchmark in vignette/compiled_models Reorganisation of the vignette generation in the Makefile. Improved YAML header in the R markdown vignettes. Rebuilt vignettes with the package installed. --- vignettes/compiled_models.html | 69 ++++++++++++++++++++++++------------------ 1 file changed, 39 insertions(+), 30 deletions(-) (limited to 'vignettes/compiled_models.html') diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html index 5fcd88fb..fc71debe 100644 --- a/vignettes/compiled_models.html +++ b/vignettes/compiled_models.html @@ -8,7 +8,9 @@ + + Performance benefit by using compiled model definitions in mkin @@ -62,6 +64,8 @@ img {
@@ -71,17 +75,17 @@ img {
- -
-

Benchmark for a model that can also be solved with Eigenvalues

-

This evaluation is taken from the example section of mkinfit. When using an mkin version equal to or greater than 0.9-36 and a compiler (gcc) is installed, you will see a message that the model is being compiled from autogenerated C code when defining a model using mkinmod.

+
+

Benchmark for a model that can also be solved with Eigenvalues

+

This evaluation is taken from the example section of mkinfit. When using an mkin version equal to or greater than 0.9-36 and a compiler (gcc) is installed, you will see a message that the model is being compiled from autogenerated C code when defining a model using mkinmod. The package tests for presence of the gcc compiler using

+
Sys.which("gcc")
+
##            gcc 
+## "/usr/bin/gcc"
+

First, we build a simple degradation model for a parent compound with one metabolite.

library("mkin")
 SFO_SFO <- mkinmod(
-  parent = list(type = "SFO", to = "m1", sink = TRUE),
-  m1 = list(type = "SFO"))
+ parent = mkinsub("SFO", "m1"), + m1 = mkinsub("SFO")) 
## Compiling differential equation model from auto-generated C code...

We can compare the performance of the Eigenvalue based solution against the compiled version and the R implementation of the differential equations using the microbenchmark package.

library("microbenchmark")
@@ -95,26 +99,26 @@ 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 6980.8002 6996.4739 7024.5611 7012.1476 7046.4415
-## Eigenvalue based       925.3350  928.9405  951.8405  932.5460  965.0932
-## deSolve, compiled      747.2635  761.9405  771.4339  776.6174  783.5191
+## deSolve, not compiled 5379.4269 5431.6605 5455.0396 5483.8940 5492.8460
+## Eigenvalue based       930.6245  951.6701  959.4653  972.7157  973.8857
+## deSolve, compiled      755.9828  771.1000  794.1810  786.2172  813.2800
 ##                             max neval
-## deSolve, not compiled 7080.7354     3
-## Eigenvalue based       997.6404     3
-## deSolve, compiled      790.4207     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:

+## deSolve, not compiled 5501.7979 3 +## Eigenvalue based 975.0556 3 +## deSolve, compiled 840.3428 3 +

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

smb.1["median"]/smb.1["deSolve, compiled", "median"]
##                         median
-## deSolve, not compiled 9.029089
-## Eigenvalue based      1.200779
+## deSolve, not compiled 6.975037
+## Eigenvalue based      1.237210
 ## deSolve, compiled     1.000000
-
-

Benchmark for a model that can not be solved with Eigenvalues

+
+

Benchmark for a model that can not be solved with Eigenvalues

This evaluation is also taken from the example section of mkinfit.

FOMC_SFO <- mkinmod(
-  parent = list(type = "FOMC", to = "m1", sink = TRUE),
-  m1 = list(type = "SFO"))
+ parent = mkinsub("FOMC", "m1"), + m1 = mkinsub( "SFO")) 
## Compiling differential equation model from auto-generated C code...
mb.2 <- microbenchmark(
   mkinfit(FOMC_SFO, FOCUS_2006_D, use_compiled = FALSE, quiet = TRUE),
@@ -123,17 +127,22 @@ print(smb.1)
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 14.127630 14.245064 14.298201 14.362497 14.383486
-## deSolve, compiled      1.354744  1.362167  1.366362  1.369589  1.372171
+
##                             min       lq     mean    median        uq
+## deSolve, not compiled 11.815894 11.84960 12.03290 11.883305 12.141404
+## deSolve, compiled      1.387086  1.43514  1.45956  1.483194  1.495796
 ##                             max neval
-## deSolve, not compiled 14.404474     3
-## deSolve, compiled      1.374752     3

+## deSolve, not compiled 12.399502     3
+## deSolve, compiled      1.508399     3
smb.2["median"]/smb.2["deSolve, compiled", "median"]
##                         median
-## deSolve, not compiled 10.48672
-## deSolve, compiled      1.00000
-

Here we get a performance benefit of a factor of 10.5 using the version of the differential equation model compiled from C code using the inline package!

+## deSolve, not compiled 8.011968 +## deSolve, compiled 1.000000 +

Here we get a performance benefit of a factor of 8 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)
+## Running under: Debian GNU/Linux 8 (jessie)
+
## CPU model: Intel(R) Core(TM) i7-4710MQ CPU @ 2.50GHz
-- cgit v1.2.1