Update benchmark results for 7950X

10 files changed, 208 insertions, 123 deletions

diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html
index d1d51ddb..43c4f1fa 100644
--- a/vignettes/FOCUS_D.html
+++ b/vignettes/FOCUS_D.html
@@ -449,10 +449,10 @@ the <code>mkinparplot</code> function.</p>
 <p>A comprehensive report of the results is obtained using the
 <code>summary</code> method for <code>mkinfit</code> objects.</p>
 <pre class="r"><code>summary(fit)</code></pre>
-<pre><code>## mkin version used for fitting:    1.3.0 
+<pre><code>## mkin version used for fitting:    1.2.2 
 ## R version used for fitting:       4.2.2 
-## Date of fit:     Fri Feb 17 10:41:52 2023 
-## Date of summary: Fri Feb 17 10:41:52 2023 
+## Date of fit:     Fri Feb 17 20:04:31 2023 
+## Date of summary: Fri Feb 17 20:04:31 2023 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html
index 190ab65b..ed150c0a 100644
--- a/vignettes/FOCUS_L.html
+++ b/vignettes/FOCUS_L.html
@@ -1561,10 +1561,10 @@ model fit. This covers the numerical analysis given in the FOCUS
 report.</p>
 <pre class="r"><code>m.L1.SFO &lt;- mkinfit(&quot;SFO&quot;, FOCUS_2006_L1_mkin, quiet = TRUE)
 summary(m.L1.SFO)</code></pre>
-<pre><code>## mkin version used for fitting:    1.3.0 
+<pre><code>## mkin version used for fitting:    1.2.2 
 ## R version used for fitting:       4.2.2 
-## Date of fit:     Fri Feb 17 10:41:53 2023 
-## Date of summary: Fri Feb 17 10:41:53 2023 
+## Date of fit:     Fri Feb 17 20:04:32 2023 
+## Date of summary: Fri Feb 17 20:04:32 2023 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
@@ -1664,17 +1664,17 @@ checked.</p>
 <pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre>
 <pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is
 ## doubtful</code></pre>
-<pre><code>## mkin version used for fitting:    1.3.0 
+<pre><code>## mkin version used for fitting:    1.2.2 
 ## R version used for fitting:       4.2.2 
-## Date of fit:     Fri Feb 17 10:41:53 2023 
-## Date of summary: Fri Feb 17 10:41:53 2023 
+## Date of fit:     Fri Feb 17 20:04:32 2023 
+## Date of summary: Fri Feb 17 20:04:32 2023 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 369 model solutions performed in 0.026 s
+## Fitted using 369 model solutions performed in 0.025 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1810,17 +1810,17 @@ plot(m.L2.FOMC, show_residuals = TRUE,
      main = &quot;FOCUS L2 - FOMC&quot;)</code></pre>
 <p><img src="data:image/png;base64,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" /><!-- --></p>
 <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre>
-<pre><code>## mkin version used for fitting:    1.3.0 
+<pre><code>## mkin version used for fitting:    1.2.2 
 ## R version used for fitting:       4.2.2 
-## Date of fit:     Fri Feb 17 10:41:54 2023 
-## Date of summary: Fri Feb 17 10:41:54 2023 
+## Date of fit:     Fri Feb 17 20:04:32 2023 
+## Date of summary: Fri Feb 17 20:04:32 2023 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 239 model solutions performed in 0.015 s
+## Fitted using 239 model solutions performed in 0.014 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1891,10 +1891,10 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
      main = &quot;FOCUS L2 - DFOP&quot;)</code></pre>
 <p><img src="data:image/png;base64,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" /><!-- --></p>
 <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre>
-<pre><code>## mkin version used for fitting:    1.3.0 
+<pre><code>## mkin version used for fitting:    1.2.2 
 ## R version used for fitting:       4.2.2 
-## Date of fit:     Fri Feb 17 10:41:54 2023 
-## Date of summary: Fri Feb 17 10:41:54 2023 
+## Date of fit:     Fri Feb 17 20:04:32 2023 
+## Date of summary: Fri Feb 17 20:04:32 2023 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -1903,7 +1903,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 581 model solutions performed in 0.04 s
+## Fitted using 581 model solutions performed in 0.039 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -2004,10 +2004,10 @@ as a row index and datasets as a column index.</p>
 using square brackets for indexing which will result in the use of the
 summary and plot functions working on mkinfit objects.</p>
 <pre class="r"><code>summary(mm.L3[[&quot;DFOP&quot;, 1]])</code></pre>
-<pre><code>## mkin version used for fitting:    1.3.0 
+<pre><code>## mkin version used for fitting:    1.2.2 
 ## R version used for fitting:       4.2.2 
-## Date of fit:     Fri Feb 17 10:41:54 2023 
-## Date of summary: Fri Feb 17 10:41:54 2023 
+## Date of fit:     Fri Feb 17 20:04:33 2023 
+## Date of summary: Fri Feb 17 20:04:33 2023 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -2126,17 +2126,17 @@ well. The error level at which the <span class="math inline"><em>χ</em><sup>2</
 slightly lower for the FOMC model. However, the difference appears
 negligible.</p>
 <pre class="r"><code>summary(mm.L4[[&quot;SFO&quot;, 1]], data = FALSE)</code></pre>
-<pre><code>## mkin version used for fitting:    1.3.0 
+<pre><code>## mkin version used for fitting:    1.2.2 
 ## R version used for fitting:       4.2.2 
-## Date of fit:     Fri Feb 17 10:41:54 2023 
-## Date of summary: Fri Feb 17 10:41:54 2023 
+## Date of fit:     Fri Feb 17 20:04:33 2023 
+## Date of summary: Fri Feb 17 20:04:33 2023 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 142 model solutions performed in 0.009 s
+## Fitted using 142 model solutions performed in 0.008 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -2190,10 +2190,10 @@ negligible.</p>
 ##        DT50 DT90
 ## parent  106  352</code></pre>
 <pre class="r"><code>summary(mm.L4[[&quot;FOMC&quot;, 1]], data = FALSE)</code></pre>
-<pre><code>## mkin version used for fitting:    1.3.0 
+<pre><code>## mkin version used for fitting:    1.2.2 
 ## R version used for fitting:       4.2.2 
-## Date of fit:     Fri Feb 17 10:41:54 2023 
-## Date of summary: Fri Feb 17 10:41:54 2023 
+## Date of fit:     Fri Feb 17 20:04:33 2023 
+## Date of summary: Fri Feb 17 20:04:33 2023 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
diff --git a/vignettes/mkin.html b/vignettes/mkin.html
index ec3bf5da..a16f3074 100644
--- a/vignettes/mkin.html
+++ b/vignettes/mkin.html
@@ -1614,7 +1614,7 @@ div.tocify {
 
 <h1 class="title toc-ignore">Introduction to mkin</h1>
 <h4 class="author">Johannes Ranke</h4>
-<h4 class="date">Last change 15 February 2021 (rebuilt 2023-02-13)</h4>
+<h4 class="date">Last change 15 February 2021 (rebuilt 2023-02-17)</h4>
 
 </div>
 
diff --git a/vignettes/web_only/benchmarks.R b/vignettes/web_only/benchmarks.R
index 6c9b133e..46081ca9 100644
--- a/vignettes/web_only/benchmarks.R
+++ b/vignettes/web_only/benchmarks.R
@@ -20,6 +20,7 @@ cpu_model <- benchmarkme::get_cpu()$model_name
 cpu_model <- gsub("AMD ", "", cpu_model)
 cpu_model <- gsub("Intel\\(R\\) Core\\(TM\\) ", "", cpu_model)
 cpu_model <- gsub(" Eight-Core Processor", "", cpu_model)
+cpu_model <- gsub(" 16-Core Processor", "", cpu_model)
 cpu_model <- gsub(" CPU @ 2.50GHz", "", cpu_model)
 
 operating_system <- Sys.info()[["sysname"]]
diff --git a/vignettes/web_only/benchmarks.html b/vignettes/web_only/benchmarks.html
index 6cce41e6..0800ac48 100644
--- a/vignettes/web_only/benchmarks.html
+++ b/vignettes/web_only/benchmarks.html
@@ -1592,7 +1592,7 @@ div.tocify {
 
 <h1 class="title toc-ignore">Benchmark timings for mkin</h1>
 <h4 class="author">Johannes Ranke</h4>
-<h4 class="date">Last change 14 July 2022 (rebuilt 2023-02-17)</h4>
+<h4 class="date">Last change 17 February 2023 (rebuilt 2023-02-17)</h4>
 
 </div>
 
@@ -1679,14 +1679,6 @@ systems. All trademarks belong to their respective owners.</p>
 <p>Constant variance (t1) and two-component error model (t2) for four
 models fitted to two datasets, i.e. eight fits for each test.</p>
 <table>
-<colgroup>
-<col width="9%" />
-<col width="48%" />
-<col width="9%" />
-<col width="13%" />
-<col width="9%" />
-<col width="10%" />
-</colgroup>
 <thead>
 <tr class="header">
 <th align="left">OS</th>
@@ -1852,19 +1844,27 @@ models fitted to two datasets, i.e. eight fits for each test.</p>
 </tr>
 <tr class="even">
 <td align="left">Linux</td>
-<td align="left">Ryzen 9 7950X 16-Core Processor</td>
+<td align="left">Ryzen 9 7950X</td>
 <td align="left">4.2.2</td>
-<td align="left">1.2.2</td>
-<td align="right">1.308</td>
-<td align="right">1.793</td>
+<td align="left">1.2.0</td>
+<td align="right">1.288</td>
+<td align="right">1.794</td>
 </tr>
 <tr class="odd">
 <td align="left">Linux</td>
-<td align="left">Ryzen 9 7950X 16-Core Processor</td>
+<td align="left">Ryzen 9 7950X</td>
 <td align="left">4.2.2</td>
-<td align="left">1.3.0</td>
-<td align="right">1.287</td>
-<td align="right">1.805</td>
+<td align="left">1.2.2</td>
+<td align="right">1.276</td>
+<td align="right">1.804</td>
+</tr>
+<tr class="even">
+<td align="left">Linux</td>
+<td align="left">Ryzen 9 7950X</td>
+<td align="left">4.2.2</td>
+<td align="left">1.2.3</td>
+<td align="right">1.327</td>
+<td align="right">1.863</td>
 </tr>
 </tbody>
 </table>
@@ -1875,15 +1875,6 @@ models fitted to two datasets, i.e. eight fits for each test.</p>
 by variable (t5) for three models fitted to one dataset, i.e. three fits
 for each test.</p>
 <table>
-<colgroup>
-<col width="8%" />
-<col width="44%" />
-<col width="8%" />
-<col width="12%" />
-<col width="8%" />
-<col width="9%" />
-<col width="8%" />
-</colgroup>
 <thead>
 <tr class="header">
 <th align="left">OS</th>
@@ -2069,21 +2060,30 @@ for each test.</p>
 </tr>
 <tr class="even">
 <td align="left">Linux</td>
-<td align="left">Ryzen 9 7950X 16-Core Processor</td>
+<td align="left">Ryzen 9 7950X</td>
 <td align="left">4.2.2</td>
-<td align="left">1.2.2</td>
-<td align="right">0.783</td>
-<td align="right">2.364</td>
-<td align="right">1.230</td>
+<td align="left">1.2.0</td>
+<td align="right">0.792</td>
+<td align="right">2.378</td>
+<td align="right">1.245</td>
 </tr>
 <tr class="odd">
 <td align="left">Linux</td>
-<td align="left">Ryzen 9 7950X 16-Core Processor</td>
+<td align="left">Ryzen 9 7950X</td>
 <td align="left">4.2.2</td>
-<td align="left">1.3.0</td>
-<td align="right">0.731</td>
-<td align="right">1.984</td>
-<td align="right">1.100</td>
+<td align="left">1.2.2</td>
+<td align="right">0.784</td>
+<td align="right">2.355</td>
+<td align="right">1.233</td>
+</tr>
+<tr class="even">
+<td align="left">Linux</td>
+<td align="left">Ryzen 9 7950X</td>
+<td align="left">4.2.2</td>
+<td align="left">1.2.3</td>
+<td align="right">0.758</td>
+<td align="right">2.050</td>
+<td align="right">1.135</td>
 </tr>
 </tbody>
 </table>
@@ -2095,16 +2095,16 @@ and variance by variable (t10 and t11) for one model fitted to one
 dataset, i.e. one fit for each test.</p>
 <table>
 <colgroup>
-<col width="6%" />
-<col width="35%" />
-<col width="6%" />
+<col width="8%" />
+<col width="19%" />
+<col width="8%" />
+<col width="12%" />
+<col width="8%" />
+<col width="8%" />
+<col width="8%" />
+<col width="9%" />
+<col width="8%" />
 <col width="9%" />
-<col width="6%" />
-<col width="6%" />
-<col width="6%" />
-<col width="7%" />
-<col width="6%" />
-<col width="7%" />
 </colgroup>
 <thead>
 <tr class="header">
@@ -2351,27 +2351,39 @@ dataset, i.e. one fit for each test.</p>
 </tr>
 <tr class="even">
 <td align="left">Linux</td>
-<td align="left">Ryzen 9 7950X 16-Core Processor</td>
+<td align="left">Ryzen 9 7950X</td>
 <td align="left">4.2.2</td>
-<td align="left">1.2.2</td>
-<td align="right">0.442</td>
-<td align="right">0.582</td>
-<td align="right">0.658</td>
-<td align="right">1.171</td>
-<td align="right">0.801</td>
-<td align="right">1.093</td>
+<td align="left">1.2.0</td>
+<td align="right">0.445</td>
+<td align="right">0.591</td>
+<td align="right">0.660</td>
+<td align="right">1.190</td>
+<td align="right">0.814</td>
+<td align="right">1.100</td>
 </tr>
 <tr class="odd">
 <td align="left">Linux</td>
-<td align="left">Ryzen 9 7950X 16-Core Processor</td>
+<td align="left">Ryzen 9 7950X</td>
 <td align="left">4.2.2</td>
-<td align="left">1.3.0</td>
-<td align="right">0.400</td>
-<td align="right">0.518</td>
-<td align="right">0.580</td>
-<td align="right">0.990</td>
-<td align="right">0.701</td>
-<td align="right">0.935</td>
+<td align="left">1.2.2</td>
+<td align="right">0.443</td>
+<td align="right">0.586</td>
+<td align="right">0.661</td>
+<td align="right">1.176</td>
+<td align="right">0.803</td>
+<td align="right">1.097</td>
+</tr>
+<tr class="even">
+<td align="left">Linux</td>
+<td align="left">Ryzen 9 7950X</td>
+<td align="left">4.2.2</td>
+<td align="left">1.2.3</td>
+<td align="right">0.414</td>
+<td align="right">0.533</td>
+<td align="right">0.598</td>
+<td align="right">1.020</td>
+<td align="right">0.726</td>
+<td align="right">0.966</td>
 </tr>
 </tbody>
 </table>
diff --git a/vignettes/web_only/benchmarks.rmd b/vignettes/web_only/benchmarks.rmd
index 117dc96a..132e5062 100644
--- a/vignettes/web_only/benchmarks.rmd
+++ b/vignettes/web_only/benchmarks.rmd
@@ -1,7 +1,7 @@
 ---
 title: "Benchmark timings for mkin"
 author: "Johannes Ranke"
-date: Last change 14 July 2022 (rebuilt `r Sys.Date()`)
+date: Last change 17 February 2023 (rebuilt `r Sys.Date()`)
 output:
   html_document:
     toc: true
@@ -47,6 +47,7 @@ cpu_model <- benchmarkme::get_cpu()$model_name
 cpu_model <- gsub("AMD ", "", cpu_model)
 cpu_model <- gsub("Intel\\(R\\) Core\\(TM\\) ", "", cpu_model)
 cpu_model <- gsub(" Eight-Core Processor", "", cpu_model)
+cpu_model <- gsub(" 16-Core Processor", "", cpu_model)
 cpu_model <- gsub(" CPU @ 2.50GHz", "", cpu_model)
 
 operating_system <- Sys.info()[["sysname"]]
diff --git a/vignettes/web_only/mkin_benchmarks.rda b/vignettes/web_only/mkin_benchmarks.rda
index a9a4adbf..0a88d04f 100644
--- a/vignettes/web_only/mkin_benchmarks.rda
+++ b/vignettes/web_only/mkin_benchmarks.rda
diff --git a/vignettes/web_only/saem_benchmarks.html b/vignettes/web_only/saem_benchmarks.html
index a3bbafef..83e70e79 100644
--- a/vignettes/web_only/saem_benchmarks.html
+++ b/vignettes/web_only/saem_benchmarks.html
@@ -1592,7 +1592,7 @@ div.tocify {
 
 <h1 class="title toc-ignore">Benchmark timings for saem.mmkin</h1>
 <h4 class="author">Johannes Ranke</h4>
-<h4 class="date">Last change 14 November 2022 (rebuilt 2023-02-17)</h4>
+<h4 class="date">Last change 17 February 2023 (rebuilt 2023-02-17)</h4>
 
 </div>
 
@@ -1815,24 +1815,44 @@ systems. All trademarks belong to their respective owners.</p>
 <td align="right">4.851</td>
 </tr>
 <tr class="odd">
-<td align="left">Ryzen 9 7950X 16-Core Processor</td>
+<td align="left">Ryzen 9 7950X</td>
+<td align="left">Linux</td>
+<td align="left">1.2.1</td>
+<td align="left">3.2</td>
+<td align="right">1.352</td>
+<td align="right">2.813</td>
+<td align="right">2.401</td>
+<td align="right">2.074</td>
+</tr>
+<tr class="even">
+<td align="left">Ryzen 9 7950X</td>
 <td align="left">Linux</td>
 <td align="left">1.2.2</td>
 <td align="left">3.2</td>
-<td align="right">1.470</td>
-<td align="right">2.263</td>
-<td align="right">1.840</td>
-<td align="right">2.299</td>
+<td align="right">1.328</td>
+<td align="right">2.738</td>
+<td align="right">2.336</td>
+<td align="right">2.023</td>
+</tr>
+<tr class="odd">
+<td align="left">Ryzen 9 7950X</td>
+<td align="left">Linux</td>
+<td align="left">1.2.3</td>
+<td align="left">3.2</td>
+<td align="right">1.711</td>
+<td align="right">2.028</td>
+<td align="right">2.868</td>
+<td align="right">2.361</td>
 </tr>
 <tr class="even">
 <td align="left">Ryzen 9 7950X 16-Core Processor</td>
 <td align="left">Linux</td>
-<td align="left">1.3.0</td>
+<td align="left">1.2.3</td>
 <td align="left">3.2</td>
-<td align="right">1.181</td>
-<td align="right">2.199</td>
-<td align="right">2.057</td>
-<td align="right">1.909</td>
+<td align="right">1.444</td>
+<td align="right">2.485</td>
+<td align="right">2.738</td>
+<td align="right">2.946</td>
 </tr>
 </tbody>
 </table>
@@ -1882,24 +1902,44 @@ systems. All trademarks belong to their respective owners.</p>
 <td align="right">8.401</td>
 </tr>
 <tr class="odd">
-<td align="left">Ryzen 9 7950X 16-Core Processor</td>
+<td align="left">Ryzen 9 7950X</td>
+<td align="left">Linux</td>
+<td align="left">1.2.1</td>
+<td align="left">3.2</td>
+<td align="right">2.388</td>
+<td align="right">3.033</td>
+<td align="right">3.532</td>
+<td align="right">3.310</td>
+</tr>
+<tr class="even">
+<td align="left">Ryzen 9 7950X</td>
 <td align="left">Linux</td>
 <td align="left">1.2.2</td>
 <td align="left">3.2</td>
-<td align="right">2.118</td>
-<td align="right">3.528</td>
-<td align="right">3.295</td>
-<td align="right">3.157</td>
+<td align="right">2.341</td>
+<td align="right">2.968</td>
+<td align="right">3.465</td>
+<td align="right">3.341</td>
+</tr>
+<tr class="odd">
+<td align="left">Ryzen 9 7950X</td>
+<td align="left">Linux</td>
+<td align="left">1.2.3</td>
+<td align="left">3.2</td>
+<td align="right">2.347</td>
+<td align="right">3.175</td>
+<td align="right">3.426</td>
+<td align="right">3.588</td>
 </tr>
 <tr class="even">
 <td align="left">Ryzen 9 7950X 16-Core Processor</td>
 <td align="left">Linux</td>
-<td align="left">1.3.0</td>
+<td align="left">1.2.3</td>
 <td align="left">3.2</td>
-<td align="right">2.384</td>
-<td align="right">3.124</td>
-<td align="right">3.484</td>
-<td align="right">3.518</td>
+<td align="right">2.228</td>
+<td align="right">3.332</td>
+<td align="right">3.257</td>
+<td align="right">3.306</td>
 </tr>
 </tbody>
 </table>
@@ -1944,20 +1984,36 @@ systems. All trademarks belong to their respective owners.</p>
 <td align="right">798.580</td>
 </tr>
 <tr class="odd">
-<td align="left">Ryzen 9 7950X 16-Core Processor</td>
+<td align="left">Ryzen 9 7950X</td>
+<td align="left">Linux</td>
+<td align="left">1.2.1</td>
+<td align="left">3.2</td>
+<td align="right">11.247</td>
+<td align="right">285.216</td>
+</tr>
+<tr class="even">
+<td align="left">Ryzen 9 7950X</td>
 <td align="left">Linux</td>
 <td align="left">1.2.2</td>
 <td align="left">3.2</td>
-<td align="right">12.336</td>
-<td align="right">277.666</td>
+<td align="right">11.242</td>
+<td align="right">284.258</td>
+</tr>
+<tr class="odd">
+<td align="left">Ryzen 9 7950X</td>
+<td align="left">Linux</td>
+<td align="left">1.2.3</td>
+<td align="left">3.2</td>
+<td align="right">11.724</td>
+<td align="right">216.900</td>
 </tr>
 <tr class="even">
 <td align="left">Ryzen 9 7950X 16-Core Processor</td>
 <td align="left">Linux</td>
-<td align="left">1.3.0</td>
+<td align="left">1.2.3</td>
 <td align="left">3.2</td>
-<td align="right">12.082</td>
-<td align="right">214.433</td>
+<td align="right">11.604</td>
+<td align="right">215.890</td>
 </tr>
 </tbody>
 </table>
@@ -1991,18 +2047,32 @@ systems. All trademarks belong to their respective owners.</p>
 <td align="right">1312.445</td>
 </tr>
 <tr class="odd">
-<td align="left">Ryzen 9 7950X 16-Core Processor</td>
+<td align="left">Ryzen 9 7950X</td>
+<td align="left">Linux</td>
+<td align="left">1.2.1</td>
+<td align="left">3.2</td>
+<td align="right">489.939</td>
+</tr>
+<tr class="even">
+<td align="left">Ryzen 9 7950X</td>
 <td align="left">Linux</td>
 <td align="left">1.2.2</td>
 <td align="left">3.2</td>
-<td align="right">459.051</td>
+<td align="right">482.970</td>
+</tr>
+<tr class="odd">
+<td align="left">Ryzen 9 7950X</td>
+<td align="left">Linux</td>
+<td align="left">1.2.3</td>
+<td align="left">3.2</td>
+<td align="right">389.119</td>
 </tr>
 <tr class="even">
 <td align="left">Ryzen 9 7950X 16-Core Processor</td>
 <td align="left">Linux</td>
-<td align="left">1.3.0</td>
+<td align="left">1.2.3</td>
 <td align="left">3.2</td>
-<td align="right">392.885</td>
+<td align="right">401.477</td>
 </tr>
 </tbody>
 </table>
diff --git a/vignettes/web_only/saem_benchmarks.rda b/vignettes/web_only/saem_benchmarks.rda
index 7d060f91..564c851b 100644
--- a/vignettes/web_only/saem_benchmarks.rda
+++ b/vignettes/web_only/saem_benchmarks.rda
diff --git a/vignettes/web_only/saem_benchmarks.rmd b/vignettes/web_only/saem_benchmarks.rmd
index 6e51fa66..6aeefd7f 100644
--- a/vignettes/web_only/saem_benchmarks.rmd
+++ b/vignettes/web_only/saem_benchmarks.rmd
@@ -1,7 +1,7 @@
 ---
 title: "Benchmark timings for saem.mmkin"
 author: "Johannes Ranke"
-date: Last change 14 November 2022 (rebuilt `r Sys.Date()`)
+date: Last change 17 February 2023 (rebuilt `r Sys.Date()`)
 output:
   html_document:
     toc: true
@@ -31,6 +31,7 @@ cpu_model <- benchmarkme::get_cpu()$model_name
 cpu_model <- gsub("AMD ", "", cpu_model)
 cpu_model <- gsub("Intel\\(R\\) Core\\(TM\\) ", "", cpu_model)
 cpu_model <- gsub(" Eight-Core Processor", "", cpu_model)
+cpu_model <- gsub(" 16-Core Processor", "", cpu_model)
 cpu_model <- gsub(" CPU @ 2.50GHz", "", cpu_model)
 
 operating_system <- Sys.info()[["sysname"]]