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Diffstat (limited to 'docs/articles/compiled_models.html')
| -rw-r--r-- | docs/articles/compiled_models.html | 30 |
1 files changed, 15 insertions, 15 deletions
diff --git a/docs/articles/compiled_models.html b/docs/articles/compiled_models.html index 87913685..d39caf12 100644 --- a/docs/articles/compiled_models.html +++ b/docs/articles/compiled_models.html @@ -54,7 +54,7 @@ <h1>Performance benefit by using compiled model definitions in mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2016-11-18</h4> + <h4 class="date">2016-12-08</h4> </div> @@ -91,21 +91,21 @@ mb<span class="fl">.1</span> <-<span class="st"> </span><span class="kw">micr <span class="kw">print</span>(mb<span class="fl">.1</span>)</code></pre></div> <pre><code>## Unit: milliseconds ## expr min lq mean median uq -## deSolve, not compiled 6126.4954 6152.1764 6170.8152 6177.8573 6192.9751 -## Eigenvalue based 864.8441 891.7069 902.0212 918.5697 920.6098 -## deSolve, compiled 706.9115 711.1015 714.9195 715.2915 718.9235 +## deSolve, not compiled 6263.2932 6273.5107 6286.8574 6283.7283 6298.6394 +## Eigenvalue based 865.5257 872.4146 892.2705 879.3036 905.6429 +## deSolve, compiled 716.7534 717.9075 724.6869 719.0617 728.6537 ## max neval cld -## 6208.0929 3 c -## 922.6498 3 b -## 722.5556 3 a</code></pre> +## 6313.5506 3 c +## 931.9821 3 b +## 738.2457 3 a</code></pre> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">autoplot</span>(mb<span class="fl">.1</span>)</code></pre></div> <p><img src="compiled_models_files/figure-html/benchmark_SFO_SFO-1.png" width="672"></p> -<p>We see that using the compiled model is by a factor of 8.6 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> +<p>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:</p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">rownames</span>(smb<span class="fl">.1</span>) <-<span class="st"> </span>smb<span class="fl">.1</span>$expr smb<span class="fl">.1</span>[<span class="st">"median"</span>]/smb<span class="fl">.1</span>[<span class="st">"deSolve, compiled"</span>, <span class="st">"median"</span>]</code></pre></div> <pre><code>## median -## deSolve, not compiled 8.636839 -## Eigenvalue based 1.284189 +## deSolve, not compiled 8.738789 +## Eigenvalue based 1.222849 ## deSolve, compiled 1.000000</code></pre> </div> <div id="model-that-can-not-be-solved-with-eigenvalues" class="section level2"> @@ -127,18 +127,18 @@ smb<span class="fl">.1</span>[<span class="st">"median"</span>]/smb<span class=" <span class="kw">print</span>(mb<span class="fl">.2</span>)</code></pre></div> <pre><code>## Unit: seconds ## expr min lq mean median uq -## deSolve, not compiled 13.163796 13.205491 13.478255 13.247187 13.635484 -## deSolve, compiled 1.267154 1.268198 1.272722 1.269242 1.275506 +## deSolve, not compiled 13.487097 13.492149 13.528060 13.497201 13.548541 +## deSolve, compiled 1.307845 1.318979 1.322967 1.330114 1.330528 ## max neval cld -## 14.023782 3 b -## 1.281771 3 a</code></pre> +## 13.599881 3 b +## 1.330941 3 a</code></pre> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">smb<span class="fl">.2</span>[<span class="st">"median"</span>]/smb<span class="fl">.2</span>[<span class="st">"deSolve, compiled"</span>, <span class="st">"median"</span>]</code></pre></div> <pre><code>## median ## 1 NA ## 2 NA</code></pre> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">autoplot</span>(mb<span class="fl">.2</span>)</code></pre></div> <p><img src="compiled_models_files/figure-html/benchmark_FOMC_SFO-1.png" width="672"></p> -<p>Here we get a performance benefit of a factor of 10.4 using the version of the differential equation model compiled from C code!</p> +<p>Here we get a performance benefit of a factor of 10.1 using the version of the differential equation model compiled from C code!</p> <p>This vignette was built with mkin 0.9.44.9000 on</p> <pre><code>## R version 3.3.2 (2016-10-31) ## Platform: x86_64-pc-linux-gnu (64-bit) |