diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/binding_and_distribution_of_tasks.md b/doc.zih.tu-dresden.de/docs/jobs_and_resources/binding_and_distribution_of_tasks.md
index 4e8bde8c6e43ab765135f3199525a09820abf8d1..3e0e107dd86e3028584739f2741db76bc923a4e8 100644
--- a/doc.zih.tu-dresden.de/docs/jobs_and_resources/binding_and_distribution_of_tasks.md
+++ b/doc.zih.tu-dresden.de/docs/jobs_and_resources/binding_and_distribution_of_tasks.md
@@ -2,44 +2,48 @@
 
 ## General
 
-To specify a pattern the commands `--cpu_bind=<cores|sockets>` and
-`--distribution=<block | cyclic>` are needed. cpu_bind defines the resolution in which the tasks
-will be allocated. While --distribution determinates the order in which the tasks will be allocated
-to the cpus.  Keep in mind that the allocation pattern also depends on your specification.
+To specify a pattern the commands `--cpu_bind=<cores|sockets>` and `--distribution=<block|cyclic>`
+are needed. The option `cpu_bind` defines the resolution in which the tasks will be allocated. While
+`--distribution` determinate the order in which the tasks will be allocated to the CPUs. Keep in
+mind that the allocation pattern also depends on your specification.
 
-```Bash
-#!/bin/bash 
-#SBATCH --nodes=2                        # request 2 nodes 
-#SBATCH --cpus-per-task=4                # use 4 cores per task 
-#SBATCH --tasks-per-node=4               # allocate 4 tasks per node - 2 per socket 
+!!! example "Explicitly specify binding and distribution"
 
-srun --ntasks 8 --cpus-per-task 4 --cpu_bind=cores --distribution=block:block ./application
-```
+    ```bash
+    #!/bin/bash
+    #SBATCH --nodes=2                        # request 2 nodes
+    #SBATCH --cpus-per-task=4                # use 4 cores per task
+    #SBATCH --tasks-per-node=4               # allocate 4 tasks per node - 2 per socket
+
+    srun --ntasks 8 --cpus-per-task 4 --cpu_bind=cores --distribution=block:block ./application
+    ```
 
 In the following sections there are some selected examples of the combinations between `--cpu_bind`
 and `--distribution` for different job types.
 
 ## MPI Strategies
 
-### Default Binding and Dsitribution Pattern
+### Default Binding and Distribution Pattern
 
-The default binding uses --cpu_bind=cores in combination with --distribution=block:cyclic. The
-default (as well as block:cyclic) allocation method will fill up one node after another, while
+The default binding uses `--cpu_bind=cores` in combination with `--distribution=block:cyclic`. The
+default (as well as `block:cyclic`) allocation method will fill up one node after another, while
 filling socket one and two in alternation. Resulting in only even ranks on the first socket of each
 node and odd on each second socket of each node.
 
-\<img alt=""
-src="data:;base64,iVBORw0KGgoAAAANSUhEUgAAAw4AAADeCAIAAAAb9sCoAAAABmJLR0QA/wD/AP+gvaeTAAAfBklEQVR4nO3dfXBU1f348bshJEA2ISGbB0gIZAMJxqciIhCktGKxaqs14UEGC9gBJVUjxIo4EwFlpiqMOgydWipazTBNVATbGevQMQQYUMdSEEUNYGIID8kmMewmm2TzeH9/3On+9pvN2T27N9nsJu/XX+Tu/dx77uee8+GTu8tiUFVVAQAAQH/ChnoAAAAAwYtWCQAAQIhWCQAAQIhWCQAAQIhWCQAAQIhWCQAAQIhWCQAAQIhWCQAAQIhWCQAAQIhWCQAAQIhWCQAAQIhWCQAAQIhWCQAAQIhWCQAAQIhWCQAAQIhWCQAAQIhWCQAAQIhWCQAAQIhWCQAAQChcT7DBYBiocQAIOaqqDvUQfEC9AkYyPfWKp0oAAABCup4qaULrN0sA+oXuExrqFTDS6K9XPFUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUCAAAQolUauX72s58ZDIZPP/3UuSU5OfnDDz+UP8KXX35pNBrl9y8uLs7JyYmKikpOTvZhoABGvMDXq40bN2ZnZ48bNy4tLW3Tpk2dnZ0+DBfDC63SiBYfH//0008H7HQmk2nDhg3btm0L2BkBDBsBrld2u33Pnj2XLl0qLS0tLS3dunVrwE6NYEOrNKKtXbu2srLygw8+cH+ptrZ26dKliYmJqampjz/+eFtbm7b90qVLd911V2xs7A033HDixAnn/s3Nzfn5+ZMnT05ISHjwwQcbGxvdj3nPPfcsW7Zs8uTJg3Q5AIaxANerN954Y8GCBfHx8Tk5OQ8//LBrOEYaWqURzWg0btu27dlnn+3q6urzUl5e3ujRoysrK0+ePHnq1KnCwkJt+9KlS1NTU+vq6v71r3/95S9/ce6/cuVKi8Vy+vTpmpqa8ePHr1mzJmBXAWAkGMJ6dfz48VmzZg3o1SCkqDroPwKG0MKFC7dv397V1TVjxozdu3erqpqUlHTw4EFVVSsqKhRFqa+v1/YsKysbM2ZMT09PRUWFwWBoamrSthcXF0dFRamqWlVVZTAYnPvbbDaDwWC1Wvs9b0lJSVJS0mBfHQZVKK79UBwznIaqXqmqumXLlvT09MbGxkG9QAwe/Ws/PNCtGYJMeHj4Sy+9tG7dulWrVjk3Xr58OSoqKiEhQfvRbDY7HI7GxsbLly/Hx8fHxcVp26dPn679obq62mAwzJ4923mE8ePHX7lyZfz48YG6DgDDX+Dr1QsvvLBv377y8vL4+PjBuioEPVolKPfff/8rr7zy0ksvObekpqa2trY2NDRo1ae6ujoyMtJkMqWkpFit1o6OjsjISEVR6urqtP3T0tIMBsOZM2fojQAMqkDWq82bNx84cODo0aOpqamDdkEIAXxWCYqiKDt37ty1a1dLS4v2Y2Zm5ty5cwsLC+12u8ViKSoqWr16dVhY2IwZM2bOnPnaa68pitLR0bFr1y5t/4yMjMWLF69du7a2tlZRlIaGhv3797ufpaenx+FwaJ8zcDgcHR0dAbo8AMNIYOpVQUHBgQMHDh06ZDKZHA4HXxYwktEqQVEUZc6cOffee6/zn40YDIb9+/e3tbWlp6fPnDnzpptuevXVV7WX3n///bKysltuueWOO+644447nEcoKSmZNGlSTk5OdHT03Llzjx8/7n6WN954Y+zYsatWrbJYLGPHjuWBNgA/BKBeWa3W3bt3X7hwwWw2jx07duzYsdnZ2YG5OgQhg/MTT/4EGwyKoug5AoBQFIprPxTHDEA//Wufp0oAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABCtEoAAABC4UM9AAAInKqqqqEeAoAQY1BV1f9gg0FRFD1HABCKQnHta2MGMDLpqVcD8FSJAgQg+JnN5qEeAoCQNABPlQCMTKH1VAkA/KOrVQIAABje+BdwAAAAQrRKAAAAQrRKAAAAQrRKAAAAQrRKAAAAQrRKAAAAQrq+gpLvVRoJ/Ps6CebGSBBaXzXCnBwJqFcQ0VOveKoEAAAgNAD/sUlo/WYJefp/02JuDFeh+1s4c3K4ol5BRP/c4KkSAACAEK0SAACAEK0SAACAEK0SAACAEK0SAACAEK0SAACAEK0SAACAEK0SAACAEK0SAACAEK0SAACAEK0SAACAEK0SAACAEK0SAACA0PBvlb799ttf//rXJpNp3LhxM2bMeOaZZ/w4yIwZMz788EPJnX/yk5+Ulpb2+1JxcXFOTk5UVFRycrIfw8DACqq5sXHjxuzs7HHjxqWlpW3atKmzs9OPwSDUBdWcpF4FlaCaGyOtXg3zVqm3t/eXv/zlpEmTvv7668bGxtLSUrPZPITjMZlMGzZs2LZt2xCOAZpgmxt2u33Pnj2XLl0qLS0tLS3dunXrEA4GQyLY5iT1KngE29wYcfVK1UH/EQbbpUuXFEX59ttv3V+6evXqkiVLEhISUlJSHnvssdbWVm37tWvX8vPz09LSoqOjZ86cWVFRoapqVlbWwYMHtVcXLly4atWqzs5Om822fv361NRUk8m0fPnyhoYGVVUff/zx0aNHm0ymKVOmrFq1qt9RlZSUJCUlDdY1Dxw995e54d/c0GzZsmXBggUDf80DJ/jvr7vgH3NwzknqVTAIzrmhGQn1apg/VZo0aVJmZub69evffffdmpoa15fy8vJGjx5dWVl58uTJU6dOFRYWattXrFhx8eLFzz77zGq1vvPOO9HR0c6Qixcvzp8///bbb3/nnXdGjx69cuVKi8Vy+vTpmpqa8ePHr1mzRlGU3bt3Z2dn7969u7q6+p133gngtcI3wTw3jh8/PmvWrIG/ZgS3YJ6TGFrBPDdGRL0a2k4tACwWy+bNm2+55Zbw8PBp06aVlJSoqlpRUaEoSn19vbZPWVnZmDFjenp6KisrFUW5cuVKn4NkZWU999xzqampe/bs0bZUVVUZDAbnEWw2m8FgsFqtqqrefPPN2llE+C0tSATh3FBVdcuWLenp6Y2NjQN4pQMuJO5vHyEx5iCck9SrIBGEc0MdMfVq+LdKTi0tLa+88kpYWNhXX331ySefREVFOV/64YcfFEWxWCxlZWXjxo1zj83KykpKSpozZ47D4dC2HD58OCwsbIqL2NjYb775RqX06I4NvOCZG88//7zZbK6urh7Q6xt4oXV/NaE15uCZk9SrYBM8c2Pk1Kth/gacK6PRWFhYOGbMmK+++io1NbW1tbWhoUF7qbq6OjIyUntTtq2trba21j18165dCQkJ9913X1tbm6IoaWlpBoPhzJkz1f9z7dq17OxsRVHCwkZQVoeHIJkbmzdv3rdv39GjR6dMmTIIV4lQEiRzEkEoSObGiKpXw3yR1NXVPf3006dPn25tbW1qanrxxRe7urpmz56dmZk5d+7cwsJCu91usViKiopWr14dFhaWkZGxePHiRx55pLa2VlXVs2fPOqdaZGTkgQMHYmJi7r777paWFm3PtWvXajs0NDTs379f2zM5OfncuXP9jqenp8fhcHR1dSmK4nA4Ojo6ApIG9CPY5kZBQcGBAwcOHTpkMpkcDsew/8e3cBdsc5J6FTyCbW6MuHo1tA+1BpvNZlu3bt306dPHjh0bGxs7f/78jz76SHvp8uXLubm5JpNp4sSJ+fn5drtd297U1LRu3bqUlJTo6Ohbbrnl3Llzqsu/Guju7v7tb3972223NTU1Wa3WgoKCqVOnGo1Gs9n85JNPakc4cuTI9OnTY2Nj8/Ly+ozn9ddfd02+64PTIKTn/jI3fJob165d67MwMzIyApcL3wX//XUX/GMOqjmpUq+CSVDNjRFYrwzOo/jBYDBop/f7CAhmeu4vc2N4C8X7G4pjhjzqFUT0399h/gYcAACAHrRKAAAAQrRKAAAAQrRKAAAAQrRKAAAAQrRKAAAAQrRKAAAAQrRKAAAAQrRKAAAAQrRKAAAAQrRKAAAAQrRKAAAAQrRKAAAAQrRKAAAAQuH6D2EwGPQfBMMScwPBhjkJEeYGRHiqBAAAIGRQVXWoxwAAABCkeKoEAAAgRKsEAAAgRKsEAAAgRKsEAAAgRKsEAAAgRKsEAAAgRKsEAAAgpOvbuvlu05HAv2/eYm6MBKH1rWzMyZGAegURPfWKp0oAAABCA/B/wIXWb5aQp/83LebGcBW6v4UzJ4cr6hVE9M8NnioBAAAI0SoBAAAI0SoBAAAI0SoBAAAI0SoBAAAI0SoBAAAI0SoBAAAI0SoBAAAIDdtW6cSJE/fee++ECROioqJuvPHGoqKi1tbWAJy3u7u7oKBgwoQJMTExK1eubG5u7nc3o9FocBEZGdnR0RGA4Y1YQzUfLBbLsmXLTCZTbGzsXXfdde7cuX53Ky4uzsnJiYqKSk5Odt2+Zs0a13lSWloagDEj8KhXcEW9CjbDs1X65z//uWjRoptvvvmzzz6rr6/ft29ffX39mTNnZGJVVe3q6vL71M8///yhQ4dOnjz5/fffX7x4cf369f3uZrFYWv4nNzf3gQceiIyM9Puk8GwI50N+fr7Vaj1//vyVK1cmTpy4dOnSfnczmUwbNmzYtm2b+0uFhYXOqbJkyRK/R4KgRb2CK+pVMFJ10H+EwdDT05OamlpYWNhne29vr6qqV69eXbJkSUJCQkpKymOPPdba2qq9mpWVVVRUdPvtt2dmZpaXl9tstvXr16empppMpuXLlzc0NGi7vfrqq1OmTBk/fvzEiRO3b9/ufvbExMS33npL+3N5eXl4ePi1a9c8jLahoSEyMvLw4cM6r3ow6Lm/wTM3hnY+ZGRk7N27V/tzeXl5WFhYd3e3aKglJSVJSUmuW1avXv3MM8/4e+mDKHjur7zgHDP1aqBQr6hXIgPQ7Qzt6QeD1n2fPn2631fnzZu3YsWK5ubm2traefPmPfroo9r2rKysG264obGxUfvxV7/61QMPPNDQ0NDW1vbII4/ce++9qqqeO3fOaDReuHBBVVWr1frf//63z8Fra2tdT609zT5x4oSH0e7cuXP69Ok6LncQDY/SM4TzQVXVTZs2LVq0yGKx2Gy2hx56KDc318NQ+y09EydOTE1NnTVr1ssvv9zZ2el7AgZF8NxfecE5ZurVQKFeUa9EaJX68cknnyiKUl9f7/5SRUWF60tlZWVjxozp6elRVTUrK+tPf/qTtr2qqspgMDh3s9lsBoPBarVWVlaOHTv2vffea25u7vfU58+fVxSlqqrKuSUsLOzjjz/2MNrMzMydO3f6fpWBMDxKzxDOB23nhQsXatm47rrrampqPAzVvfQcOnTo008/vXDhwv79+1NSUtx/1xwqwXN/5QXnmKlXA4V6pW2nXrnTf3+H4WeVEhISFEW5cuWK+0uXL1+OiorSdlAUxWw2OxyOxsZG7cdJkyZpf6iurjYYDLNnz546derUqVNvuumm8ePHX7lyxWw2FxcX//nPf05OTv7pT3969OjRPsePjo5WFMVms2k/trS09Pb2xsTEvP32285PurnuX15eXl1dvWbNmoG6drgbwvmgquqdd95pNpubmprsdvuyZctuv/321tZW0Xxwt3jx4nnz5k2bNi0vL+/ll1/et2+fnlQgCFGv4Ip6FaSGtlMbDNp7vU899VSf7b29vX268vLy8sjISGdXfvDgQW37999/P2rUKKvVKjpFW1vbH//4x7i4OO39Y1eJiYl/+9vftD8fOXLE83v/y5cvf/DBB327vADSc3+DZ24M4XxoaGhQ3N7g+Pzzz0XHcf8tzdV77703YcIET5caQMFzf+UF55ipVwOFeqVtp165G4BuZ2hPP0j+8Y9/jBkz5rnnnqusrHQ4HGfPns3Pzz9x4kRvb+/cuXMfeuihlpaWurq6+fPnP/LII1qI61RTVfXuu+9esmTJ1atXVVWtr69///33VVX97rvvysrKHA6HqqpvvPFGYmKie+kpKirKysqqqqqyWCwLFixYsWKFaJD19fURERHB+QFJzfAoPeqQzocpU6asW7fOZrO1t7e/8MILRqOxqanJfYTd3d3t7e3FxcVJSUnt7e3aMXt6evbu3VtdXW21Wo8cOZKRkeH8aMKQC6r7Kylox0y9GhDUK+cRqFd90CoJHT9+/O67746NjR03btyNN9744osvav9Y4PLly7m5uSaTaeLEifn5+Xa7Xdu/z1SzWq0FBQVTp041Go1ms/nJJ59UVfXUqVO33XZbTExMXFzcnDlzjh075n7ezs7OJ554IjY21mg0rlixwmaziUa4Y8eOoP2ApGbYlB516ObDmTNnFi9eHBcXFxMTM2/ePNHfNK+//rrrs96oqChVVXt6eu688874+PiIiAiz2fzss8+2tbUNeGb8E2z3V0Ywj5l6pR/1yhlOvepD//01OI/iB+2dSz1HQDDTc3+ZG8NbKN7fUBwz5FGvIKL//g7Dj3UDAAAMFFolAAAAIVolAAAAIVolAAAAIVolAAAAIVolAAAAIVolAAAAIVolAAAAIVolAAAAoXD9h/D6vw1jxGJuINgwJyHC3IAIT5UAAACEdP0fcAAAAMMbT5UAAACEaJUAAACEaJUAAACEaJUAAACEaJUAAACEaJUAAACEaJUAAACEdH1bN99tOhL4981bzI2RILS+lY05ORJQryCip17xVAkAAEBoAP4POD1dPLHBH6tHKF4vsfKxoSgU80ysfKweoXi9xMrH6sFTJQAAACFaJQAAACFaJQAAAKFBaZW6u7sLCgomTJgQExOzcuXK5uZm+diNGzdmZ2ePGzcuLS1t06ZNnZ2dfpx95syZBoOhrq7Op8B///vfc+bMGTNmTEJCwqZNm+QDLRbLsmXLTCZTbGzsXXfdde7cOc/7FxcX5+TkREVFJScn9xm517yJYmXyJop1nt2/vPnE8xg8KyoqSk9Pj4yMjI+Pv++++77//nv52DVr1hhclJaWyscajUbX2MjIyI6ODsnYy5cv5+XlxcfHT5gw4fe//73XQFF+ZPIm2kcmb6JYPXkLZh7y6bUOiGJl6oBoncqsfVGszNr3vI/nte8h1muuRLEyuRLNWz1/v8gQ3V+ZOiCKlakDolzJrH1RrMzaF8XKrH1RrEyuRLEyuRJdl56/X7xQdRAdoaioKDMzs7Ky0mKxzJ8/f8WKFfKxa9euPXbsWGNj44kTJyZPnrx582b5WM327dsXLVqkKEptba18bFlZmdFo/Otf/1pXV1dTU3Ps2DH52AceeOAXv/jFjz/+aLfbV69efeONN3qO/eijj959990dO3YkJSW57iPKm0ysKG8ysRr3vOmZIaJYz2PwHPv5559XVlY2NzdXVVXdf//9OTk58rGrV68uLCxs+Z+uri75WLvd7gzMzc1dvny5fOxtt9324IMP2my2q1evzp0798knn/QcK8qPaLtMrChvMrGivOmvHoEnc72iOiATK6oDrrGidSqz9kWxMmvfc131vPZFsTK5EsXK5Eo0b2Vy5SuZ+yuqAzKxojogkyuZtS+KlVn7oliZtS+KlcmVKFYmV6LrksmVfwalVUpMTHzrrbe0P5eXl4eHh1+7dk0y1tWWLVsWLFggf15VVb/55puMjIwvvvhC8bFVysnJeeaZZzyPRxSbkZGxd+9e7c/l5eVhYWHd3d1eY0tKSvrcTlHeZGJdueZNMrbfvA1U6XHnefxez9vZ2Zmfn3/PPffIx65evdrv++vU0NAQGRl5+PBhydgrV64oilJRUaH9ePDgQaPR2NHR4TVWlB/37T7NjT55k4kV5U1/6Qk8mesV1QGZWFEdEOXKdZ3Kr333WNF2yVif1r5rrHyu3GN9ylWfeetrrmT4tI761AGvsR7qgPz9lVn7olhVYu27x/q69vs9r9dc9Yn1NVf9/l0gnyt5A/8GXF1dXX19/cyZM7UfZ82a1d3d/e233/pxqOPHj8+aNUt+/56ent/97nevvfZadHS0TydyOByff/55T0/PddddFxcXt2jRoq+++ko+PC8vr6SkpL6+vrm5+c033/zNb34zatQonwaghGbeAq+4uDg5OTk6Ovrrr7/++9//7mvs5MmTb7311h07dnR1dflx9rfffjstLe3nP/+55P7OJepkt9t9et9woAxt3kJFgOuAc536sfZFa1xm7bvu4+vad8b6kSvX80rmyn3eDmCd9FsA6oCvNdxDrE9r3z1Wfu33O2bJXDlj5XOlp6b5Q0+f1e8Rzp8/ryhKVVXV/2/HwsI+/vhjmVhXW7ZsSU9Pb2xslDyvqqo7d+5cunSpqqrfffed4stTpdraWkVR0tPTz549a7fbN2zYkJKSYrfbJc9rs9kWLlyovXrdddfV1NTInLdP5+shb15jXfXJm0ysKG96ZojnWL+fKrW1tV29evXYsWMzZ85cu3atfOyhQ4c+/fTTCxcu7N+/PyUlpbCw0Ncxq6qamZm5c+dOn8Z86623Oh8mz5s3T1GUzz77zGvsgD9V6jdvMrGivOmvHoHn9Xo91AGZXInqQL+5cl2nPq19VVwbva599318WvuusT7lyv28krlyn7e+5kqSTzW2Tx2QiRXVAfn7K/mkxD1Wcu27x/q09kVz0muu3GMlc+Xh74LBeKo08K2StoROnz6t/ah95u7EiRMysU7PP/+82Wyurq6WP++FCxcmTZpUV1en+t4qtbS0KIqyY8cO7cf29vZRo0YdPXpUJra3t3f27NkPP/xwU1OT3W7funVrWlqaTJvVb5nuN2/yy9g9b15jPeRtYEuPzPjlz3vs2DGDwdDa2upH7L59+xITE3097+HDhyMiIhoaGnwa88WLF3Nzc5OSktLT07du3aooyvnz573GDtIbcOr/zZuvsa550196As/r9XqoA15jPdQB99g+69SntS+qjTJrv88+Pq39PrE+5apPrE+50jjnrU+5kie/FtzrgEysqA7I31+Zte/5703Pa99zrOe1L4qVyZV7rHyu3K9LExpvwCUnJycmJn755Zfaj6dOnQoPD8/OzpY/wubNm/ft23f06NEpU6bIRx0/fryxsfH66683mUxaK3r99de/+eabMrFGo3HatGnOL/T06Zs9f/zxx//85z8FBQVxcXFRUVFPPfVUTU3N2bNn5Y+gCcW8Da1Ro0b58UanoigRERHd3d2+Ru3Zsyc3N9dkMvkUlZaW9sEHH9TV1VVVVaWmpqakpEybNs3XUw+sAOcthASmDrivU/m1L1rjMmvffR/5te8eK58r91j/aqY2b/XXSZ0GtQ74V8PlY0Vr32ush7XvIdZrrvqN9aNm+l3TfKCnzxIdoaioKCsrq6qqymKxLFiwwKd/AffEE09Mnz69qqqqvb29vb3d/TOwotjW1tZL/3PkyBFFUU6dOiX/Jtqrr75qNpvPnTvX3t7+hz/8YfLkyfJPLKZMmbJu3Tqbzdbe3v7CCy8YjcampiYPsd3d3e3t7cXFxUlJSe3t7Q6HQ9suyptMrChvXmM95E3PDBHFisbvNbazs/PFF1+sqKiwWq1ffPHFrbfempeXJxnb09Ozd+/e6upqq9V65MiRjIyMRx99VH7MqqrW19dHRET0+4Fuz7EnT5784YcfGhsbDxw4kJCQ8Pbbb3uOFeVHtN1rrIe8eY31kDf91SPwZPIsqgMysaI64BorWqcya18UK7P2+91Hcu2Lji+TK1Gs11x5mLcyuRqMuaEK6oBMrKgOyORKZu33Gyu59vuNlVz7Hv6+9porUazXXHm4Lplc+WdQWqXOzs4nnngiNjbWaDSuWLHCZrNJxl67dk35vzIyMuTP6+TrG3Cqqvb29m7ZsiUpKSkmJuaOO+74+uuv5WPPnDmzePHiuLi4mJiYefPmef0XUq+//rrrNUZFRWnbRXnzGushbzLnFeVNz/QSxXodgyi2q6vrvvvuS0pKioiImDp16saNG+XnVU9Pz5133hkfHx8REWE2m5999tm2tjb5MauqumPHjunTp/f7kufYXbt2JSYmjh49Ojs7u7i42GusKD+i7V5jPeTNa6yHvOmZG0NFJs+iOiATK6oDzlgP69Tr2hfFyqx9mboqWvseYr3mykOs11x5mLcyddJXMvdXFdQBmVhRHZDJlde1L4qVWfuiWJm173leec6Vh1ivufJwXTJ10j8G51H8ELr/bR6xxBI7VLFDJRRzRSyxxA5trIb/2AQAAECIVgkAAECIVgkAAECIVgkAAEBoAD7WjeFNz8foMLyF4se6MbxRryDCx7oBAAAGha6nSgAAAMMbT5UAAACEaJUAAACEaJUAAACEaJUAAACEaJUAAACEaJUAAACEaJUAAACEaJUAAACEaJUAAACEaJUAAACEaJUAAACEaJUAAACEaJUAAACEaJUAAACEaJUAAACEaJUAAACEaJUAAACEaJUAAACEaJUAAACE/h82xQH7rLtt0wAAAABJRU5ErkJggg=="
-/>
+![Default distribution](misc/mpi_default.png)
+{: align="center"}
 
-```Bash
-#!/bin/bash
-#SBATCH --nodes=2
-#SBATCH --tasks-per-node=16
-#SBATCH --cpus-per-task=1
 
-srun --ntasks 32 ./application
-```
+!!! example "Default binding and default distribution"
+
+    ```bash
+    #!/bin/bash
+    #SBATCH --nodes=2
+    #SBATCH --tasks-per-node=16
+    #SBATCH --cpus-per-task=1
+
+    srun --ntasks 32 ./application
+    ```
 
 ### Core Bound
 
@@ -50,18 +54,19 @@ application.
 
 This method allocates the tasks linearly to the cores.
 
-\<img alt=""
-src="data:;base64,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"
-/>
+![block:block distribution](misc/mpi_block_block.png)
+{: align="center"}
 
-```Bash
-#!/bin/bash
-#SBATCH --nodes=2
-#SBATCH --tasks-per-node=16
-#SBATCH --cpus-per-task=1
+!!! example "Binding to cores and block:block distribution"
 
-srun --ntasks 32 --cpu_bind=cores --distribution=block:block ./application
-```
+    ```bash
+    #!/bin/bash
+    #SBATCH --nodes=2
+    #SBATCH --tasks-per-node=16
+    #SBATCH --cpus-per-task=1
+
+    srun --ntasks 32 --cpu_bind=cores --distribution=block:block ./application
+    ```
 
 #### Distribution: cyclic:cyclic
 
@@ -71,18 +76,19 @@ then the first socket of the second node until one task is placed on
 every first socket of every node. After that it will place a task on
 every second socket of every node and so on.
 
-\<img alt=""
-src="<data:;base64,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>"
-/>
+![cyclic:cyclic distribution](misc/mpi_cyclic_cyclic.png)
+{: align="center"}
 
-```Bash
-#!/bin/bash
-#SBATCH --nodes=2
-#SBATCH --tasks-per-node=16
-#SBATCH --cpus-per-task=1
+!!! example "Binding to cores and cyclic:cyclic distribution"
+
+    ```bash
+    #!/bin/bash
+    #SBATCH --nodes=2
+    #SBATCH --tasks-per-node=16
+    #SBATCH --cpus-per-task=1
 
-srun --ntasks 32 --cpu_bind=cores --distribution=cyclic:cyclic
-```
+    srun --ntasks 32 --cpu_bind=cores --distribution=cyclic:cyclic
+    ```
 
 #### Distribution: cyclic:block
 
@@ -90,104 +96,108 @@ The cyclic:block distribution will allocate the tasks of your job in
 alternation on node level, starting with first node filling the sockets
 linearly.
 
-\<img alt=""
-src="<data:;base64,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>"
-/>
+![cyclic:block distribution](misc/mpi_cyclic_block.png)
+{: align="center"}
 
+!!! example "Binding to cores and cyclic:block distribution"
+
+    ```bash
     #!/bin/bash
     #SBATCH --nodes=2
     #SBATCH --tasks-per-node=16
     #SBATCH --cpus-per-task=1
 
     srun --ntasks 32 --cpu_bind=cores --distribution=cyclic:block ./application
+    ```
 
 ### Socket Bound
 
-Note: The general distribution onto the nodes and sockets stays the
-same. The mayor difference between socket and cpu bound lies within the
-ability of the tasks to "jump" from one core to another inside a socket
-while executing the application. These jumps can slow down the execution
-time of your application.
+The general distribution onto the nodes and sockets stays the same. The mayor difference between
+socket- and CPU-bound lies within the ability of the OS to move tasks from one core to another
+inside a socket while executing the application. These jumps can slow down the execution time of
+your application.
 
 #### Default Distribution
 
-The default distribution uses --cpu_bind=sockets with
---distribution=block:cyclic. The default allocation method (as well as
-block:cyclic) will fill up one node after another, while filling socket
-one and two in alternation. Resulting in only even ranks on the first
-socket of each node and odd on each second socket of each node.
+The default distribution uses `--cpu_bind=sockets` with `--distribution=block:cyclic`. The default
+allocation method (as well as `block:cyclic`) will fill up one node after another, while filling
+socket one and two in alternation. Resulting in only even ranks on the first socket of each node and
+odd on each second socket of each node.
 
-\<img alt=""
-src="data:;base64,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"
-/>
+![Binding to sockets and block:cyclic distribution](misc/mpi_socket_block_cyclic.png)
+{: align="center"}
 
-```Bash
-#!/bin/bash
-#SBATCH --nodes=2
-#SBATCH --tasks-per-node=16
-#SBATCH --cpus-per-task=1
+!!! example "Binding to sockets and block:cyclic distribution"
 
-srun --ntasks 32 -cpu_bind=sockets ./application
-```
+    ```bash
+    #!/bin/bash
+    #SBATCH --nodes=2
+    #SBATCH --tasks-per-node=16
+    #SBATCH --cpus-per-task=1
+
+    srun --ntasks 32 -cpu_bind=sockets ./application
+    ```
 
 #### Distribution: block:block
 
 This method allocates the tasks linearly to the cores.
 
-\<img alt=""
-src="data:;base64,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"
-/>
+![Binding to sockets and block:block distribution](misc/mpi_socket_block_block.png)
+{: align="center"}
+
+!!! example "Binding to sockets and block:block distribution"
 
-```Bash
-#!/bin/bash
-#SBATCH --nodes=2
-#SBATCH --tasks-per-node=16
-#SBATCH --cpus-per-task=1
+    ```bash
+    #!/bin/bash
+    #SBATCH --nodes=2
+    #SBATCH --tasks-per-node=16
+    #SBATCH --cpus-per-task=1
 
-srun --ntasks 32 --cpu_bind=sockets --distribution=block:block ./application
-```
+    srun --ntasks 32 --cpu_bind=sockets --distribution=block:block ./application
+    ```
 
 #### Distribution: block:cyclic
 
-The block:cyclic distribution will allocate the tasks of your job in
+The `block:cyclic` distribution will allocate the tasks of your job in
 alternation between the first node and the second node while filling the
 sockets linearly.
 
-\<img alt=""
-src="data:;base64,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"
-/>
+![Binding to sockets and block:cyclic distribution](misc/mpi_socket_block_cyclic.png)
+{: align="center"}
+
+!!! example "Binding to sockets and block:cyclic distribution"
 
+    ```bash
     #!/bin/bash
     #SBATCH --nodes=2
     #SBATCh --tasks-per-node=16
     #SBATCH --cpus-per-task=1
 
     srun --ntasks 32 --cpu_bind=sockets --distribution=block:cyclic ./application
+    ```
 
 ## Hybrid Strategies
 
 ### Default Binding and Distribution Pattern
 
-The default binding pattern of hybrid jobs will split the cores
-allocated to a rank between the sockets of a node. The example shows
-that Rank 0 has 4 cores at its disposal. Two of them on first socket
-inside the first node and two on the second socket inside the first
-node.
+The default binding pattern of hybrid jobs will split the cores allocated to a rank between the
+sockets of a node. The example shows that Rank 0 has 4 cores at its disposal. Two of them on first
+socket inside the first node and two on the second socket inside the first node.
 
-\<img alt=""
-src="data:;base64,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"
-/>
+![sockets binding and block:block distribution](misc/hybrid.png)
+{: align="center"}
 
-```Bash
-#!/bin/bash
-#SBATCH --nodes=2
-#SBATCH --tasks-per-node=4
-#SBATCH --cpus-per-task=4
+!!! example "Binding to sockets and block:block distribution"
 
-export OMP_NUM_THREADS=4
+    ```bash
+    #!/bin/bash
+    #SBATCH --nodes=2
+    #SBATCH --tasks-per-node=4
+    #SBATCH --cpus-per-task=4
 
-srun --ntasks 8 --cpus-per-task $OMP_NUM_THREADS ./application
-```
+    export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
+    srun --ntasks 8 --cpus-per-task $OMP_NUM_THREADS ./application
+    ```
 
 ### Core Bound
 
@@ -195,36 +205,37 @@ srun --ntasks 8 --cpus-per-task $OMP_NUM_THREADS ./application
 
 This method allocates the tasks linearly to the cores.
 
-\<img alt=""
-src="<data:;base64,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>"
-/>
+![Binding to cores and block:block distribution](misc/hybrid_cores_block_block.png)
+{: align="center"}
 
-```Bash
-#!/bin/bash
-#SBATCH --nodes=2
-#SBATCH --tasks-per-node=4
-#SBATCH --cpus-per-task=4
+!!! example "Binding to cores and block:block distribution"
 
-export OMP_NUM_THREADS=4
+    ```bash
+    #!/bin/bash
+    #SBATCH --nodes=2
+    #SBATCH --tasks-per-node=4
+    #SBATCH --cpus-per-task=4
 
-srun --ntasks 8 --cpus-per-task $OMP_NUM_THREADS --cpu_bind=cores --distribution=block:block ./application
-```
+    export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
+    srun --ntasks 8 --cpus-per-task $OMP_NUM_THREADS --cpu_bind=cores --distribution=block:block ./application
+    ```
 
 #### Distribution: cyclic:block
 
-The cyclic:block distribution will allocate the tasks of your job in
-alternation between the first node and the second node while filling the
-sockets linearly.
+The `cyclic:block` distribution will allocate the tasks of your job in alternation between the first
+node and the second node while filling the sockets linearly.
+
+![binding to cores and cyclic:block distribution](misc/hybrid_cores_cyclic_block.png)
+{: align="center"}
 
-\<img alt=""
-src="data:;base64,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"
-/>
+!!! example "Binding to cores and cyclic:block distribution"
 
-```Bash
-#!/bin/bash
-#SBATCH --nodes=2
-#SBATCH --tasks-per-node=4
-#SBATCH --cpus-per-task=4
+    ```bash
+    #!/bin/bash
+    #SBATCH --nodes=2
+    #SBATCH --tasks-per-node=4
+    #SBATCH --cpus-per-task=4
 
-export OMP_NUM_THREADS=4<br /><br />srun --ntasks 8 --cpus-per-task $OMP_NUM_THREADS --cpu_bind=cores --distribution=cyclic:block ./application
-```
+    export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
+    srun --ntasks 8 --cpus-per-task $OMP_NUM_THREADS --cpu_bind=cores --distribution=cyclic:block ./application
+    ```
diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/hybrid_cores_block_block.png b/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/hybrid_cores_block_block.png
new file mode 100644
index 0000000000000000000000000000000000000000..4c196df91b2fe410609a8e76505eca95f283ce29
Binary files /dev/null and b/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/hybrid_cores_block_block.png differ
diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/hybrid_cores_cyclic_block.png b/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/hybrid_cores_cyclic_block.png
new file mode 100644
index 0000000000000000000000000000000000000000..dfccaf451553c710fcddd648ae9721866668f9e8
Binary files /dev/null and b/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/hybrid_cores_cyclic_block.png differ
diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/mpi_block_block.png b/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/mpi_block_block.png
new file mode 100644
index 0000000000000000000000000000000000000000..0c6e9bbfa0e7f0614ede7e89f292e2d5f1a74316
Binary files /dev/null and b/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/mpi_block_block.png differ
diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/mpi_cyclic_block.png b/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/mpi_cyclic_block.png
new file mode 100644
index 0000000000000000000000000000000000000000..dab17e83ed4930b253818e15bc42ef1b1b2c9918
Binary files /dev/null and b/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/mpi_cyclic_block.png differ
diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/mpi_cyclic_cyclic.png b/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/mpi_cyclic_cyclic.png
new file mode 100644
index 0000000000000000000000000000000000000000..8b9361dd1f0a2b76b063ad64652844c425aacbdf
Binary files /dev/null and b/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/mpi_cyclic_cyclic.png differ
diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/mpi_default.png b/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/mpi_default.png
new file mode 100644
index 0000000000000000000000000000000000000000..82087209059e535401724c493fff74d743da58e4
Binary files /dev/null and b/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/mpi_default.png differ
diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/mpi_socket_block_block.png b/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/mpi_socket_block_block.png
new file mode 100644
index 0000000000000000000000000000000000000000..be12c78d1a85297cd60161a1808462941def94fb
Binary files /dev/null and b/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/mpi_socket_block_block.png differ
diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/mpi_socket_block_cyclic.png b/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/mpi_socket_block_cyclic.png
new file mode 100644
index 0000000000000000000000000000000000000000..08f2a90100ed88175f7ef6fa3d867a70ad0880d7
Binary files /dev/null and b/doc.zih.tu-dresden.de/docs/jobs_and_resources/misc/mpi_socket_block_cyclic.png differ