Performance benefit by using compiled model definitions in mkin

Johannes Ranke

2018-09-14

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", quietly = TRUE)
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"])
} else {
  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   2.205    1.000     2.203        0
## 1 deSolve, not compiled            3  17.206    7.803    17.196        0
## 2      Eigenvalue based            3   2.829    1.283     2.828        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 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

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"])
} else {
  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   3.874    1.000     3.866    0.004
## 1 deSolve, not compiled            3  35.712    9.218    35.692    0.000
##   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 0.9.47.3 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