Performance benefit by using compiled model definitions in mkin

- -

- - - - - - - + + + +

+ + + + +

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")

+Model that can also be solved with Eigenvalues

Sys.which("gcc")

##            gcc 
-## "/usr/bin/gcc"

+## "/usr/bin/gcc"

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

library("mkin")

## Loading required package: minpack.lm

## Loading required package: rootSolve

## Loading required package: inline

## Loading required package: methods

## Loading required package: parallel

SFO_SFO <- mkinmod(
-  parent = mkinsub("SFO", "m1"),
-  m1 = mkinsub("SFO"))

library("mkin")

## Lade nötiges Paket: minpack.lm

## Lade nötiges Paket: rootSolve

## Lade nötiges Paket: inline

## Lade nötiges Paket: methods

## Lade nötiges Paket: parallel

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 benchmark package.

if (require(rbenchmark)) {
-  b.1 <- benchmark(
-    "deSolve, not compiled" = mkinfit(SFO_SFO, FOCUS_2006_D,
-                                      solution_type = "deSolve",
-                                      use_compiled = FALSE, quiet = TRUE),
-    "Eigenvalue based" = mkinfit(SFO_SFO, FOCUS_2006_D,
-                                 solution_type = "eigen", quiet = TRUE),
-    "deSolve, compiled" = mkinfit(SFO_SFO, FOCUS_2006_D,
-                                  solution_type = "deSolve", quiet = TRUE),
-    replications = 3)
-  print(b.1)
-  factor_SFO_SFO <- round(b.1["1", "relative"])
+if (require(rbenchmark)) {
+  b.1 <- benchmark(
+    "deSolve, not compiled" = mkinfit(SFO_SFO, FOCUS_2006_D,
+                                      solution_type = "deSolve",
+                                      use_compiled = FALSE, quiet = TRUE),
+    "Eigenvalue based" = mkinfit(SFO_SFO, FOCUS_2006_D,
+                                 solution_type = "eigen", quiet = TRUE),
+    "deSolve, compiled" = mkinfit(SFO_SFO, FOCUS_2006_D,
+                                  solution_type = "deSolve", quiet = TRUE),
+    replications = 3)
+  print(b.1)
+  factor_SFO_SFO <- round(b.1["1", "relative"])
 } else {
-  factor_SFO_SFO <- NA
-  print("R package benchmark is not available")
-}
-## Loading required package: rbenchmark
+  factor_SFO_SFO <- NA
+  print("R package benchmark is not available")
+}

## Lade nötiges Paket: rbenchmark

##                    test replications elapsed relative user.self sys.self
-## 3     deSolve, compiled            3   2.040    1.000     2.040        0
-## 1 deSolve, not compiled            3  14.622    7.168    14.624        0
-## 2      Eigenvalue based            3   2.478    1.215     2.480        0
+## 3     deSolve, compiled            3   2.038    1.000     2.036        0
+## 1 deSolve, not compiled            3  14.822    7.273    14.824        0
+## 2      Eigenvalue based            3   2.543    1.248     2.544        0
 ##   user.child sys.child
 ## 3          0         0
 ## 1          0         0
@@ -268,59 +137,65 @@ div.tocify {
 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.


 
-Model that can not be solved with Eigenvalues
+
+Model that can not be solved with Eigenvalues
 This evaluation is also taken from the example section of mkinfit.
-if (require(rbenchmark)) {
-  FOMC_SFO <- mkinmod(
-    parent = mkinsub("FOMC", "m1"),
-    m1 = mkinsub( "SFO"))
-
-  b.2 <- benchmark(
-    "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D,
-                                      use_compiled = FALSE, quiet = TRUE),
-    "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
-    replications = 3)
-  print(b.2)
-  factor_FOMC_SFO <- round(b.2["1", "relative"])
+if (require(rbenchmark)) {
+  FOMC_SFO <- mkinmod(
+    parent = mkinsub("FOMC", "m1"),
+    m1 = mkinsub( "SFO"))
+
+  b.2 <- benchmark(
+    "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D,
+                                      use_compiled = FALSE, quiet = TRUE),
+    "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
+    replications = 3)
+  print(b.2)
+  factor_FOMC_SFO <- round(b.2["1", "relative"])
 } else {
-  factor_FOMC_SFO <- NA
-  print("R package benchmark is not available")
-}
+  factor_FOMC_SFO <- NA
+  print("R package benchmark is not available")
+}
 ## Successfully compiled differential equation model from auto-generated C code.
 ##                    test replications elapsed relative user.self sys.self
-## 2     deSolve, compiled            3   3.500    1.000     3.500        0
-## 1 deSolve, not compiled            3  29.932    8.552    29.932        0
+## 2     deSolve, compiled            3   3.488    1.000     3.488        0
+## 1 deSolve, not compiled            3  30.604    8.774    30.600        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 0.9.45 on
-## R version 3.4.0 (2017-04-21)
+This vignette was built with mkin 0.9.46.1 on
+## R version 3.4.1 (2017-06-30)
 ## Platform: x86_64-pc-linux-gnu (64-bit)
-## Running under: Debian GNU/Linux 8 (jessie)
+## Running under: Debian GNU/Linux 9 (stretch)
 ## CPU model: Intel(R) Core(TM) i7-4710MQ CPU @ 2.50GHz


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+      Contents
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+Model that can also be solved with Eigenvalues
+      Model that can not be solved with Eigenvalues
+      
 
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-- 
cgit v1.2.1

Performance benefit by using compiled model definitions in mkin

Johannes Ranke

2017-05-05

Performance benefit by using compiled model definitions in mkin

Johannes Ranke

2017-09-14

Model that can also be solved with Eigenvalues

+Model that can also be solved with Eigenvalues

Model that can not be solved with Eigenvalues

+Model that can not be solved with Eigenvalues

Contents