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 greater than 0.9-36 and the ccSolve package is installed and functional, you will get a message that the model is being compiled when defining a model using mkinmod.

library("mkin")
+SFO_SFO <- mkinmod(
+  parent = list(type = "SFO", to = "m1", sink = TRUE),
+  m1 = list(type = "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")
+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 6192.0125 6195.3470 6211.0309 6198.6816 6220.5401
+## Eigenvalue based       956.7604 1008.7224 1026.2572 1060.6844 1061.0055
+## deSolve, compiled      869.6880  871.9315  883.4929  874.1751  890.3953
+##                             max neval
+## deSolve, not compiled 6242.3986     3
+## Eigenvalue based      1061.3266     3
+## deSolve, compiled      906.6155     3

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

smb.1["median"]/smb.1["deSolve, compiled", "median"]

##                         median
+## deSolve, not compiled 7.120877
+## Eigenvalue based      1.205328
+## 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 = list(type = "FOMC", to = "m1", sink = TRUE),
+  m1 = list(type = "SFO"))

## Compiling 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 13.297283 13.427702 13.481155 13.558121 13.573092
+## deSolve, compiled      1.486926  1.526887  1.546851  1.566848  1.576813
+##                             max neval
+## deSolve, not compiled 13.588063     3
+## deSolve, compiled      1.586778     3

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

##                         median
+## deSolve, not compiled 8.653119
+## deSolve, compiled     1.000000

Here we get a performance benefit of more than a factor of 8 using the version of the differential equation model compiled from C code using the ccSolve package!

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