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Diffstat (limited to 'vignettes/compiled_models.html')
| -rw-r--r-- | vignettes/compiled_models.html | 63 |
1 files changed, 28 insertions, 35 deletions
diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html index 2f2a6edb..efdbe20d 100644 --- a/vignettes/compiled_models.html +++ b/vignettes/compiled_models.html @@ -77,37 +77,30 @@ img { --> <div id="benchmark-for-a-model-that-can-also-be-solved-with-eigenvalues" class="section level1"> <h1>Benchmark for a model that can also be solved with Eigenvalues</h1> -<p>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.</p> +<p>This evaluation is taken from the example section of mkinfit. When using an mkin version greater or equal than 0.9-36 and the C++ compiler g++ is installed and functional (on Windows, install Rtools), you will get a message that the model is being compiled when defining a model using mkinmod.</p> <pre class="r"><code>library("mkin") SFO_SFO <- mkinmod( parent = list(type = "SFO", to = "m1", sink = TRUE), - m1 = list(type = "SFO"))</code></pre> -<pre><code>## Compiling differential equation model from auto-generated C code...</code></pre> + m1 = list(type = "SFO"), odeintr_compile = "yes")</code></pre> +<pre><code>## Compiling differential equation model from auto-generated C++ code...</code></pre> <p>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.</p> -<pre class="r"><code>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") +<pre class="r"><code>smb.1 <- summary(mb.1)[-1] +rownames(smb.1) <- c("deSolve, not compiled", "Eigenvalue based", "odeintr, compiled") print(smb.1)</code></pre> <pre><code>## 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 +## deSolve, not compiled 5254.1030 5261.3501 5277.1074 5268.5973 5288.6096 +## Eigenvalue based 897.1575 921.6935 930.9546 946.2296 947.8531 +## odeintr, compiled 693.6001 709.0719 719.5530 724.5438 732.5295 ## max neval -## deSolve, not compiled 6242.3986 3 -## Eigenvalue based 1061.3266 3 -## deSolve, compiled 906.6155 3</code></pre> -<p>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:</p> -<pre class="r"><code>smb.1["median"]/smb.1["deSolve, compiled", "median"]</code></pre> +## deSolve, not compiled 5308.6218 3 +## Eigenvalue based 949.4766 3 +## odeintr, compiled 740.5151 3</code></pre> +<p>We see that using the compiled model is more than a factor of 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:</p> +<pre class="r"><code>smb.1["median"]/smb.1["odeintr, compiled", "median"]</code></pre> <pre><code>## median -## deSolve, not compiled 7.120877 -## Eigenvalue based 1.205328 -## deSolve, compiled 1.000000</code></pre> +## deSolve, not compiled 7.290796 +## Eigenvalue based 1.370242 +## odeintr, compiled 1.000000</code></pre> </div> <div id="benchmark-for-a-model-that-can-not-be-solved-with-eigenvalues" class="section level1"> <h1>Benchmark for a model that can not be solved with Eigenvalues</h1> @@ -115,25 +108,25 @@ print(smb.1)</code></pre> <pre class="r"><code>FOMC_SFO <- mkinmod( parent = list(type = "FOMC", to = "m1", sink = TRUE), m1 = list(type = "SFO"))</code></pre> -<pre><code>## Compiling differential equation model from auto-generated C code...</code></pre> +<pre><code>## Compiling differential equation model from auto-generated C++ code...</code></pre> <pre class="r"><code>mb.2 <- microbenchmark( - mkinfit(FOMC_SFO, FOCUS_2006_D, use_compiled = FALSE, quiet = TRUE), - mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE), + mkinfit(FOMC_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet = TRUE), + mkinfit(FOMC_SFO, FOCUS_2006_D, solution_type = "odeintr", quiet = TRUE), times = 3, control = list(warmup = 1)) smb.2 <- summary(mb.2)[-1] -rownames(smb.2) <- c("deSolve, not compiled", "deSolve, compiled") +rownames(smb.2) <- c("deSolve, not compiled", "odeintr, compiled") print(smb.2)</code></pre> <pre><code>## 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 +## deSolve, not compiled 11.243675 11.324875 11.382415 11.406074 11.451785 +## odeintr, compiled 1.207114 1.209908 1.239989 1.212703 1.256426 ## max neval -## deSolve, not compiled 13.588063 3 -## deSolve, compiled 1.586778 3</code></pre> -<pre class="r"><code>smb.2["median"]/smb.2["deSolve, compiled", "median"]</code></pre> +## deSolve, not compiled 11.497496 3 +## odeintr, compiled 1.300149 3</code></pre> +<pre class="r"><code>smb.2["median"]/smb.2["odeintr, compiled", "median"]</code></pre> <pre><code>## median -## deSolve, not compiled 8.653119 -## deSolve, compiled 1.000000</code></pre> -<p>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!</p> +## deSolve, not compiled 9.405494 +## odeintr, compiled 1.000000</code></pre> +<p>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 odeintr package!</p> </div> |