From 38f9e15f0c972c1516ae737a2bca8d7789581bbd Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 6 Oct 2016 09:19:21 +0200 Subject: Static documentation rebuilt by pkgdown::build_site() --- docs/articles/compiled_models.html | 146 +++++++++++++++++++++++++++++++++++++ 1 file changed, 146 insertions(+) create mode 100644 docs/articles/compiled_models.html (limited to 'docs/articles/compiled_models.html') diff --git a/docs/articles/compiled_models.html b/docs/articles/compiled_models.html new file mode 100644 index 00000000..1eeb78c6 --- /dev/null +++ b/docs/articles/compiled_models.html @@ -0,0 +1,146 @@ + +Performance benefit by using compiled model definitions in mkin. mkin +
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+ +
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+

Performance benefit by using compiled model definitions in mkin

+

Johannes Ranke

+ +

2016-10-06

+
+ + + +
+

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")
+library("ggplot2")
+mb.1 <- microbenchmark(
+  "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),
+  times = 3, control = list(warmup = 0))
+
## Warning in microbenchmark(`deSolve, not compiled` = mkinfit(SFO_SFO,
+## FOCUS_2006_D, : Could not measure overhead. Your clock might lack
+## precision.
+
smb.1 <- summary(mb.1)
+print(mb.1)
+
## Unit: milliseconds
+##                   expr       min        lq      mean    median        uq
+##  deSolve, not compiled 6407.0333 6420.1971 6434.6510 6433.3609 6448.4598
+##       Eigenvalue based  887.4338  891.8401  906.9270  896.2463  916.6735
+##      deSolve, compiled  720.2433  727.8793  733.2019  735.5152  739.6812
+##        max neval cld
+##  6463.5587     3   c
+##   937.1007     3  b 
+##   743.8472     3 a
+
autoplot(mb.1)
+

+

We see that using the compiled model is by a factor of 8.7 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:

+
rownames(smb.1) <- smb.1$expr
+smb.1["median"]/smb.1["deSolve, compiled", "median"]
+
##                         median
+## deSolve, not compiled 8.746741
+## Eigenvalue based      1.218529
+## 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(
+  "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, 
+                                    use_compiled = FALSE, quiet = TRUE),
+  "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
+  times = 3, control = list(warmup = 0))
+
## Warning in microbenchmark(`deSolve, not compiled` = mkinfit(FOMC_SFO,
+## FOCUS_2006_D, : Could not measure overhead. Your clock might lack
+## precision.
+
smb.2 <- summary(mb.2)
+print(mb.2)
+
## Unit: seconds
+##                   expr       min       lq      mean    median        uq
+##  deSolve, not compiled 13.501761 13.52142 13.697021 13.541086 13.794651
+##      deSolve, compiled  1.359921  1.35996  1.366796  1.359999  1.370233
+##        max neval cld
+##  14.048217     3   b
+##   1.380468     3  a
+
smb.2["median"]/smb.2["deSolve, compiled", "median"]
+
##   median
+## 1     NA
+## 2     NA
+
autoplot(mb.2)
+

+

Here we get a performance benefit of a factor of 10 using the version of the differential equation model compiled from C code!

+

This vignette was built with mkin 0.9.44.9000 on

+
## R version 3.3.1 (2016-06-21)
+## 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