Performance benefit by using compiled model definitions in mkin

mkin version: 0.9.41 R version: 3.2.2 -Date of fit: Mon Nov 9 08:25:33 2015 -Date of summary: Mon Nov 9 08:25:33 2015 +Date of fit: Mon Nov 9 09:06:14 2015 +Date of summary: Mon Nov 9 09:06:14 2015 Equations: d_parent = - k_parent_sink * parent - k_parent_m1 * parent @@ -144,7 +144,7 @@ d_m1 = + k_parent_m1 * parent - k_m1_sink * m1 Model predictions using solution type deSolve -Fitted with method Port using 153 model solutions performed in 0.626 s +Fitted with method Port using 153 model solutions performed in 0.619 s Weighting: none diff --git a/inst/web/vignettes/FOCUS_Z.pdf b/inst/web/vignettes/FOCUS_Z.pdf index a84b8c94..1234de20 100644 Binary files a/inst/web/vignettes/FOCUS_Z.pdf and b/inst/web/vignettes/FOCUS_Z.pdf differ diff --git a/inst/web/vignettes/compiled_models.html b/inst/web/vignettes/compiled_models.html new file mode 100644 index 00000000..92919da9 --- /dev/null +++ b/inst/web/vignettes/compiled_models.html @@ -0,0 +1,162 @@ + + + + + + + + + + + + + + +Performance benefit by using compiled model definitions in mkin + + + + + + + + + + + + + + + + + + + + + + +

+ + + + +

Benchmark for a model that can also be solved with Eigenvalues
Benchmark for a model that can not be solved with Eigenvalues

+ +

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 C compiler (gcc) is available, you will see a message that the model is being compiled from autogenerated C code when defining a model using mkinmod. The mkinmod() function checks 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 = mkinsub("SFO", "m1"),
+  m1 = mkinsub("SFO"))

## Successfully compiled 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")
+mb.1 <- microbenchmark(
+  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", use_compiled = FALSE, 
+          quiet = TRUE),
+  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet = TRUE),
+  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet = TRUE),
+  times = 3, control = list(warmup = 1))
+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 9307.3194 9319.9546 9332.8171 9332.5899 9345.5659
+## Eigenvalue based       855.3608  855.8081  869.4725  856.2555  876.5283
+## deSolve, compiled      686.6143  687.9256  698.0279  689.2369  703.7346
+##                             max neval cld
+## deSolve, not compiled 9358.5420     3   c
+## Eigenvalue based       896.8012     3  b 
+## deSolve, compiled      718.2324     3 a

We see that using the compiled model is by a factor of 13.5 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 13.540468
+## Eigenvalue based       1.242324
+## deSolve, compiled      1.000000

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 = mkinsub("FOMC", "m1"),
+  m1 = mkinsub( "SFO"))

## Successfully compiled differential equation model from auto-generated C code.

mb.2 <- microbenchmark(
+  mkinfit(FOMC_SFO, FOCUS_2006_D, use_compiled = FALSE, quiet = TRUE),
+  mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
+  times = 3, control = list(warmup = 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 20.130709 20.147223 20.180429 20.163737 20.205289
+## deSolve, compiled      1.235864  1.255748  1.267458  1.275632  1.283255
+##                             max neval cld
+## deSolve, not compiled 20.246841     3   b
+## deSolve, compiled      1.290878     3  a

smb.2["median"]/smb.2["deSolve, compiled", "median"]

##                         median
+## deSolve, not compiled 15.80686
+## deSolve, compiled      1.00000

Here we get a performance benefit of a factor of 15.8 using the version of the differential equation model compiled from C code using the inline package!

This vignette was built with mkin 0.9.41 on

## R version 3.2.2 (2015-08-14)
+## 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

+ + +

+ + + + + + diff --git a/inst/web/vignettes/mkin.pdf b/inst/web/vignettes/mkin.pdf index ce628c0e..9c1a03f3 100644 Binary files a/inst/web/vignettes/mkin.pdf and b/inst/web/vignettes/mkin.pdf differ -- cgit v1.2.1

Vignettes

Performance benefit by using compiled model definitions in mkin

Johannes Ranke

2015-11-09

Benchmark for a model that can also be solved with Eigenvalues

Benchmark for a model that can not be solved with Eigenvalues