diff --git a/Figures.ipynb b/Figures.ipynb
index 904b155..ec2d441 100644
--- a/Figures.ipynb
+++ b/Figures.ipynb
@@ -65,91 +65,148 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "     t_init    t_total                                            outfile  \\\n",
-      "0  1.248385  20.045867  /work/martinls/230527/ShallowWaterGPU/mpi_out_...   \n",
-      "1  1.687006  21.810200  /work/martinls/230528/ShallowWaterGPU/mpi_out_...   \n",
-      "2  2.178354  24.593490  /work/martinls/230530/ShallowWaterGPU/mpi_out_...   \n",
-      "3  2.690906  25.624513  /work/martinls/230531/ShallowWaterGPU/mpi_out_...   \n",
-      "4  3.629718  26.697773  /work/martinls/230532/ShallowWaterGPU/mpi_out_...   \n",
-      "5  4.364927  27.958164  /work/martinls/230533/ShallowWaterGPU/mpi_out_...   \n",
-      "6  5.628270  29.105025  /work/martinls/230534/ShallowWaterGPU/mpi_out_...   \n",
-      "7  6.777608  30.504384  /work/martinls/230535/ShallowWaterGPU/mpi_out_...   \n",
+      "       t_init     t_total                                            outfile  \\\n",
+      "0   14.511714  181.754037  /work/martinls/232557/ShallowWaterGPU/mpi_out_...   \n",
+      "1   15.153404  188.838794  /work/martinls/232558/ShallowWaterGPU/mpi_out_...   \n",
+      "2   15.607471  190.535054  /work/martinls/232589/ShallowWaterGPU/mpi_out_...   \n",
+      "3   15.332916  188.146165  /work/martinls/232590/ShallowWaterGPU/mpi_out_...   \n",
+      "4   15.941363  193.263406  /work/martinls/232591/ShallowWaterGPU/mpi_out_...   \n",
+      "5   16.805506  194.776481  /work/martinls/232592/ShallowWaterGPU/mpi_out_...   \n",
+      "6   18.009921  198.615131  /work/martinls/232593/ShallowWaterGPU/mpi_out_...   \n",
+      "7   17.990572  199.018155  /work/martinls/232594/ShallowWaterGPU/mpi_out_...   \n",
+      "8   19.366701  202.898836  /work/martinls/232595/ShallowWaterGPU/mpi_out_...   \n",
+      "9   19.890607  205.122811  /work/martinls/232596/ShallowWaterGPU/mpi_out_...   \n",
+      "10  20.974516  207.287065  /work/martinls/232597/ShallowWaterGPU/mpi_out_...   \n",
+      "11  21.358601  209.105944  /work/martinls/232598/ShallowWaterGPU/mpi_out_...   \n",
+      "12  22.813077  211.172879  /work/martinls/232599/ShallowWaterGPU/mpi_out_...   \n",
+      "13  23.636758  212.722331  /work/martinls/232600/ShallowWaterGPU/mpi_out_...   \n",
+      "14  23.983026  214.176335  /work/martinls/232601/ShallowWaterGPU/mpi_out_...   \n",
+      "15  24.996966  216.951382  /work/martinls/232602/ShallowWaterGPU/mpi_out_...   \n",
       "\n",
-      "   t_sim_init  t_nc_write  t_full_step  t_mpi_halo_exchange  \\\n",
-      "0    1.880793   12.403532     4.007889                  0.0   \n",
-      "1    2.117109   12.576457     4.909249                  0.0   \n",
-      "2    2.050483   12.774502     7.045701                  0.0   \n",
-      "3    2.216515   13.096246     7.060501                  0.0   \n",
-      "4    2.259021   13.178762     7.057118                  0.0   \n",
-      "5    2.455840   13.353797     7.218295                  0.0   \n",
-      "6    2.354878   13.576300     6.985424                  0.0   \n",
-      "7    2.688699   13.492631     6.996821                  0.0   \n",
+      "    t_sim_init  t_nc_write  t_full_step  t_mpi_halo_exchange  \\\n",
+      "0    10.661480  113.172576    42.137838                  0.0   \n",
+      "1    11.083883  118.234985    43.038861                  0.0   \n",
+      "2    11.338173  118.849141    43.378157                  0.0   \n",
+      "3    11.166394  116.970772    43.362903                  0.0   \n",
+      "4    11.167876  121.090511    43.696337                  0.0   \n",
+      "5    11.125732  122.019746    43.435468                  0.0   \n",
+      "6    11.410769  124.265493    43.508696                  0.0   \n",
+      "7    11.951049  123.907622    43.785883                  0.0   \n",
+      "8    11.861801  126.177618    44.059032                  0.0   \n",
+      "9    12.045421  127.249941    44.542234                  0.0   \n",
+      "10   12.357193  128.412160    44.133266                  0.0   \n",
+      "11   12.668238  129.337771    44.327086                  0.0   \n",
+      "12   12.733378  129.754346    44.384927                  0.0   \n",
+      "13   12.836023  130.674045    44.157766                  0.0   \n",
+      "14   13.105231  131.080429    44.535530                  0.0   \n",
+      "15   13.106097  133.506058    43.892579                  0.0   \n",
       "\n",
-      "   t_mpi_halo_exchange_download  t_mpi_halo_exchange_upload  \\\n",
-      "0                      3.883057                    0.025879   \n",
-      "1                      4.622559                    0.027954   \n",
-      "2                      3.596680                    0.027832   \n",
-      "3                      6.201660                    0.028931   \n",
-      "4                      3.875732                    0.027222   \n",
-      "5                      4.124268                    0.028076   \n",
-      "6                      4.145630                    0.028564   \n",
-      "7                      5.710327                    0.030151   \n",
+      "    t_mpi_halo_exchange_download  t_mpi_halo_exchange_upload  \\\n",
+      "0                      41.482056                    0.042358   \n",
+      "1                      41.775146                    0.042603   \n",
+      "2                      41.762573                    0.041992   \n",
+      "3                      41.740112                    0.041138   \n",
+      "4                      41.728638                    0.043213   \n",
+      "5                      41.725586                    0.044678   \n",
+      "6                      41.731934                    0.044067   \n",
+      "7                      41.630493                    0.043823   \n",
+      "8                      41.810547                    0.044678   \n",
+      "9                      41.643677                    0.044678   \n",
+      "10                     41.851196                    0.045288   \n",
+      "11                     41.774414                    0.046509   \n",
+      "12                     41.790405                    0.046509   \n",
+      "13                     41.642212                    0.046387   \n",
+      "14                     41.643066                    0.045044   \n",
+      "15                     41.756714                    0.047485   \n",
       "\n",
-      "   t_mpi_halo_exchange_sendreceive  t_mpi_step      nx      ny        dt  \\\n",
-      "0                         0.015381    0.017944  8192.0  8192.0  0.000001   \n",
-      "1                         0.122803    0.018860  8192.0  8192.0  0.000001   \n",
-      "2                         3.337158    0.019775  8192.0  8192.0  0.000001   \n",
-      "3                         0.542480    0.019165  8192.0  8192.0  0.000001   \n",
-      "4                         0.423584    0.020264  8192.0  8192.0  0.000001   \n",
-      "5                         2.685059    0.019531  8192.0  8192.0  0.000001   \n",
-      "6                         0.510254    0.019775  8192.0  8192.0  0.000001   \n",
-      "7                         0.753418    0.018982  8192.0  8192.0  0.000001   \n",
+      "    t_mpi_halo_exchange_sendreceive  t_mpi_step       nx       ny        dt  \\\n",
+      "0                          0.059082    0.025330  22528.0  22528.0  0.000001   \n",
+      "1                          0.402832    0.026062  22528.0  22528.0  0.000001   \n",
+      "2                          0.779541    0.026123  22528.0  22528.0  0.000001   \n",
+      "3                          1.217041    0.025879  22528.0  22528.0  0.000001   \n",
+      "4                          1.111328    0.026855  22528.0  22528.0  0.000001   \n",
+      "5                          0.885742    0.027466  22528.0  22528.0  0.000001   \n",
+      "6                          0.954346    0.027405  22528.0  22528.0  0.000001   \n",
+      "7                          1.984375    0.028320  22528.0  22528.0  0.000001   \n",
+      "8                          1.729980    0.027954  22528.0  22528.0  0.000001   \n",
+      "9                          1.878174    0.028931  22528.0  22528.0  0.000001   \n",
+      "10                         1.613525    0.029053  22528.0  22528.0  0.000001   \n",
+      "11                         1.831299    0.028137  22528.0  22528.0  0.000001   \n",
+      "12                         1.806152    0.029480  22528.0  22528.0  0.000001   \n",
+      "13                         1.662354    0.030518  22528.0  22528.0  0.000001   \n",
+      "14                         1.943604    0.029297  22528.0  22528.0  0.000001   \n",
+      "15                         0.937256    0.030579  22528.0  22528.0  0.000001   \n",
       "\n",
-      "   n_time_steps  slurm_job_id n_cuda_devices  n_processes  \\\n",
-      "0         200.0      230527.0              1            1   \n",
-      "1         200.0      230528.0              2            2   \n",
-      "2         200.0      230530.0              3            3   \n",
-      "3         200.0      230531.0              4            4   \n",
-      "4         200.0      230532.0              5            5   \n",
-      "5         200.0      230533.0              6            6   \n",
-      "6         200.0      230534.0              7            7   \n",
-      "7         200.0      230535.0              8            8   \n",
+      "    n_time_steps  slurm_job_id n_cuda_devices  n_processes  \\\n",
+      "0          200.0      232557.0              1            1   \n",
+      "1          200.0      232558.0              2            2   \n",
+      "2          200.0      232589.0              3            3   \n",
+      "3          200.0      232590.0              4            4   \n",
+      "4          200.0      232591.0              5            5   \n",
+      "5          200.0      232592.0              6            6   \n",
+      "6          200.0      232593.0              7            7   \n",
+      "7          200.0      232594.0              8            8   \n",
+      "8          200.0      232595.0              9            9   \n",
+      "9          200.0      232596.0             10           10   \n",
+      "10         200.0      232597.0             11           11   \n",
+      "11         200.0      232598.0             12           12   \n",
+      "12         200.0      232599.0             13           13   \n",
+      "13         200.0      232600.0             14           14   \n",
+      "14         200.0      232601.0             15           15   \n",
+      "15         200.0      232602.0             16           16   \n",
       "\n",
-      "                                     git_hash  \\\n",
-      "0  0f0cbad2dd661c59f9a2c43740eda12d90cca413\\n   \n",
-      "1  0f0cbad2dd661c59f9a2c43740eda12d90cca413\\n   \n",
-      "2  0f0cbad2dd661c59f9a2c43740eda12d90cca413\\n   \n",
-      "3  0f0cbad2dd661c59f9a2c43740eda12d90cca413\\n   \n",
-      "4  0f0cbad2dd661c59f9a2c43740eda12d90cca413\\n   \n",
-      "5  0f0cbad2dd661c59f9a2c43740eda12d90cca413\\n   \n",
-      "6  0f0cbad2dd661c59f9a2c43740eda12d90cca413\\n   \n",
-      "7  0f0cbad2dd661c59f9a2c43740eda12d90cca413\\n   \n",
+      "                                      git_hash  \\\n",
+      "0   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "1   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "2   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "3   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "4   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "5   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "6   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "7   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "8   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "9   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "10  aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "11  aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "12  aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "13  aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "14  aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "15  aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
       "\n",
-      "                                          git_status  \n",
-      "0   M Figures.ipynb\\n M dgx-2_strong_scaling_benc...  \n",
-      "1   M Figures.ipynb\\n M dgx-2_strong_scaling_benc...  \n",
-      "2   M Figures.ipynb\\n M dgx-2_strong_scaling_benc...  \n",
-      "3   M Figures.ipynb\\n M dgx-2_strong_scaling_benc...  \n",
-      "4   M Figures.ipynb\\n M dgx-2_strong_scaling_benc...  \n",
-      "5   M Figures.ipynb\\n M dgx-2_strong_scaling_benc...  \n",
-      "6   M Figures.ipynb\\n M dgx-2_strong_scaling_benc...  \n",
-      "7   M Figures.ipynb\\n M dgx-2_strong_scaling_benc...  \n"
+      "                                           git_status  \n",
+      "0    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "1    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "2    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "3    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "4    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "5    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "6    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "7    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "8    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "9    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "10   M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "11   M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "12   M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "13   M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "14   M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "15   M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n"
      ]
     }
    ],
    "source": [
     "# DGX-2\n",
     "#weak_scaling_profiling_data = read_profiling_files(\"output_dgx-2/weak_scaling/2022-06-09T134809/\")\n",
+    "weak_scaling_profiling_data = read_profiling_files(\"output_dgx-2/weak_scaling/2022-06-23T154025/\")\n",
     "\n",
     "# HGX\n",
-    "weak_scaling_profiling_data = read_profiling_files(\"output_hgx/weak_scaling/2022-06-16T162931/\")\n",
+    "#weak_scaling_profiling_data = read_profiling_files(\"output_hgx/weak_scaling/2022-06-16T162931/\")\n",
     "##weak_scaling_profiling_data = read_profiling_files(\"output_hgx/weak_scaling/2022-06-16T170630/\")\n",
     "\n",
     "print(weak_scaling_profiling_data)"
@@ -186,70 +243,127 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "     t_init     t_total                                            outfile  \\\n",
-      "0  8.105802  127.329448  /work/martinls/230507/ShallowWaterGPU/mpi_out_...   \n",
-      "1  8.391940  106.173041  /work/martinls/230508/ShallowWaterGPU/mpi_out_...   \n",
-      "2  8.316061   89.259504  /work/martinls/230509/ShallowWaterGPU/mpi_out_...   \n",
-      "3  9.480870   82.180610  /work/martinls/230510/ShallowWaterGPU/mpi_out_...   \n",
-      "4  9.948056   74.482449  /work/martinls/230511/ShallowWaterGPU/mpi_out_...   \n",
+      "       t_init     t_total                                            outfile  \\\n",
+      "0   15.227155  189.004926  /work/martinls/232634/ShallowWaterGPU/mpi_out_...   \n",
+      "1   12.726498  145.335962  /work/martinls/232635/ShallowWaterGPU/mpi_out_...   \n",
+      "2   11.482033  123.139408  /work/martinls/232636/ShallowWaterGPU/mpi_out_...   \n",
+      "3   10.548483  100.839853  /work/martinls/232637/ShallowWaterGPU/mpi_out_...   \n",
+      "4   10.746949   95.866956  /work/martinls/232638/ShallowWaterGPU/mpi_out_...   \n",
+      "5   10.345715   87.113081  /work/martinls/232639/ShallowWaterGPU/mpi_out_...   \n",
+      "6    9.915406   75.785243  /work/martinls/232640/ShallowWaterGPU/mpi_out_...   \n",
+      "7   10.107682   69.963608  /work/martinls/232641/ShallowWaterGPU/mpi_out_...   \n",
+      "8   10.620777   66.039795  /work/martinls/232642/ShallowWaterGPU/mpi_out_...   \n",
+      "9   11.305829   63.000684  /work/martinls/232643/ShallowWaterGPU/mpi_out_...   \n",
+      "10  11.614343   60.330283  /work/martinls/232644/ShallowWaterGPU/mpi_out_...   \n",
+      "11  12.639043   60.506280  /work/martinls/232645/ShallowWaterGPU/mpi_out_...   \n",
+      "12  13.312508   57.034760  /work/martinls/232646/ShallowWaterGPU/mpi_out_...   \n",
       "\n",
-      "   t_sim_init  t_nc_write  t_full_step  t_mpi_halo_exchange  \\\n",
-      "0    5.656313   88.769145    23.461966                  0.0   \n",
-      "1    5.297291   72.174575    19.195057                  0.0   \n",
-      "2    5.045456   58.199751    16.024106                  0.0   \n",
-      "3    5.172412   52.463597    13.905023                  0.0   \n",
-      "4    4.827947   46.293962    12.370357                  0.0   \n",
+      "    t_sim_init  t_nc_write  t_full_step  t_mpi_halo_exchange  \\\n",
+      "0    11.414952  118.087399    42.976445                  0.0   \n",
+      "1     8.819334   90.167300    32.226894                  0.0   \n",
+      "2     7.751206   75.901292    26.841162                  0.0   \n",
+      "3     6.980121   58.661561    23.018016                  0.0   \n",
+      "4     6.335883   57.172819    20.371334                  0.0   \n",
+      "5     5.870950   51.546669    18.195306                  0.0   \n",
+      "6     5.579971   41.915547    16.725574                  0.0   \n",
+      "7     5.234274   38.144568    14.960167                  0.0   \n",
+      "8     4.945005   35.074090    13.968068                  0.0   \n",
+      "9     4.773231   32.496020    13.152020                  0.0   \n",
+      "10    4.734492   30.088176    11.919627                  0.0   \n",
+      "11    4.422556   30.348880    11.168828                  0.0   \n",
+      "12    4.536324   26.665879    10.616396                  0.0   \n",
       "\n",
-      "   t_mpi_halo_exchange_download  t_mpi_halo_exchange_upload  \\\n",
-      "0                     21.429688                    0.028931   \n",
-      "1                     15.628418                    0.031372   \n",
-      "2                     13.573486                    0.030273   \n",
-      "3                     11.412964                    0.030151   \n",
-      "4                     10.445801                    0.030762   \n",
+      "    t_mpi_halo_exchange_download  t_mpi_halo_exchange_upload  \\\n",
+      "0                      41.536133                    0.042114   \n",
+      "1                      31.025757                    0.041748   \n",
+      "2                      25.926025                    0.039062   \n",
+      "3                      22.155762                    0.040649   \n",
+      "4                      19.375610                    0.040161   \n",
+      "5                      17.366577                    0.039062   \n",
+      "6                      15.636230                    0.040527   \n",
+      "7                      14.279663                    0.040649   \n",
+      "8                      13.050293                    0.039307   \n",
+      "9                      11.995850                    0.039917   \n",
+      "10                     11.195801                    0.039429   \n",
+      "11                     10.509277                    0.039307   \n",
+      "12                      9.817139                    0.040283   \n",
       "\n",
-      "   t_mpi_halo_exchange_sendreceive  t_mpi_step       nx       ny        dt  \\\n",
-      "0                         1.946533    0.019531  41984.0  10496.0  0.000001   \n",
-      "1                         2.726074    0.021606  41984.0   8396.0  0.000001   \n",
-      "2                         1.489014    0.020386  41984.0   6997.0  0.000001   \n",
-      "3                         1.407959    0.019775  41984.0   5997.0  0.000001   \n",
-      "4                         1.264648    0.021240  41984.0   5248.0  0.000001   \n",
+      "    t_mpi_halo_exchange_sendreceive  t_mpi_step       nx       ny        dt  \\\n",
+      "0                          1.334229    0.025146  45056.0  11264.0  0.000001   \n",
+      "1                          0.792480    0.026306  45056.0   8396.0  0.000001   \n",
+      "2                          0.567139    0.025024  45056.0   6997.0  0.000001   \n",
+      "3                          0.596924    0.025452  45056.0   5997.0  0.000001   \n",
+      "4                          0.803955    0.024841  45056.0   5248.0  0.000001   \n",
+      "5                          0.732422    0.025330  45056.0   4664.0  0.000001   \n",
+      "6                          0.979492    0.026062  45056.0   4198.0  0.000001   \n",
+      "7                          0.487793    0.025635  45056.0   3816.0  0.000001   \n",
+      "8                          0.795654    0.024780  45056.0   3498.0  0.000001   \n",
+      "9                          0.995605    0.025330  45056.0   3229.0  0.000001   \n",
+      "10                         0.691406    0.025452  45056.0   2998.0  0.000001   \n",
+      "11                         0.388672    0.025757  45056.0   2798.0  0.000001   \n",
+      "12                         0.655518    0.025146  45056.0   2624.0  0.000001   \n",
       "\n",
-      "   n_time_steps  slurm_job_id n_cuda_devices  n_processes  \\\n",
-      "0         200.0      230507.0              4            4   \n",
-      "1         200.0      230508.0              5            5   \n",
-      "2         200.0      230509.0              6            6   \n",
-      "3         200.0      230510.0              7            7   \n",
-      "4         200.0      230511.0              8            8   \n",
+      "    n_time_steps  slurm_job_id n_cuda_devices  n_processes  \\\n",
+      "0          200.0      232634.0              4            4   \n",
+      "1          200.0      232635.0              5            5   \n",
+      "2          200.0      232636.0              6            6   \n",
+      "3          200.0      232637.0              7            7   \n",
+      "4          200.0      232638.0              8            8   \n",
+      "5          200.0      232639.0              9            9   \n",
+      "6          200.0      232640.0             10           10   \n",
+      "7          200.0      232641.0             11           11   \n",
+      "8          200.0      232642.0             12           12   \n",
+      "9          200.0      232643.0             13           13   \n",
+      "10         200.0      232644.0             14           14   \n",
+      "11         200.0      232645.0             15           15   \n",
+      "12         200.0      232646.0             16           16   \n",
       "\n",
-      "                                     git_hash  \\\n",
-      "0  0f0cbad2dd661c59f9a2c43740eda12d90cca413\\n   \n",
-      "1  0f0cbad2dd661c59f9a2c43740eda12d90cca413\\n   \n",
-      "2  0f0cbad2dd661c59f9a2c43740eda12d90cca413\\n   \n",
-      "3  0f0cbad2dd661c59f9a2c43740eda12d90cca413\\n   \n",
-      "4  0f0cbad2dd661c59f9a2c43740eda12d90cca413\\n   \n",
+      "                                      git_hash  \\\n",
+      "0   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "1   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "2   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "3   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "4   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "5   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "6   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "7   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "8   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "9   aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "10  aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "11  aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
+      "12  aa693a9a468e3d591417342d96128d90c9df7884\\n   \n",
       "\n",
-      "                                          git_status  \n",
-      "0   M Figures.ipynb\\n M dgx-2_strong_scaling_benc...  \n",
-      "1   M Figures.ipynb\\n M dgx-2_strong_scaling_benc...  \n",
-      "2   M Figures.ipynb\\n M dgx-2_strong_scaling_benc...  \n",
-      "3   M Figures.ipynb\\n M dgx-2_strong_scaling_benc...  \n",
-      "4   M Figures.ipynb\\n M dgx-2_strong_scaling_benc...  \n"
+      "                                           git_status  \n",
+      "0    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "1    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "2    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "3    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "4    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "5    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "6    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "7    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "8    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "9    M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "10   M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "11   M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n",
+      "12   M Figures.ipynb\\n M dgx-2_scaling_benchmark.j...  \n"
      ]
     }
    ],
    "source": [
     "# DGX-2\n",
     "#strong_scaling_profiling_data = read_profiling_files(\"output_dgx-2/strong_scaling/2022-06-09T160712/\")\n",
+    "strong_scaling_profiling_data = read_profiling_files(\"output_dgx-2/strong_scaling/2022-06-23T172838/\")\n",
     "\n",
     "# HGX\n",
-    "strong_scaling_profiling_data = read_profiling_files(\"output_hgx/strong_scaling/2022-06-16T152945/\")\n",
+    "#strong_scaling_profiling_data = read_profiling_files(\"output_hgx/strong_scaling/2022-06-16T152945/\")\n",
     "\n",
     "print(strong_scaling_profiling_data)"
    ]
@@ -263,12 +377,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1152x432 with 2 Axes>"
       ]
@@ -280,6 +394,11 @@
     }
    ],
    "source": [
+    "plt.rcParams['font.size'] = 16\n",
+    "plt.rcParams['legend.fontsize'] = 14\n",
+    "plt.rcParams['axes.linewidth'] = 2\n",
+    "plt.rcParams['lines.linewidth'] = 2\n",
+    "\n",
     "fig, (ax_weak, ax_strong) = plt.subplots(1, 2, figsize=(16,6))\n",
     "\n",
     "t_total_no_init_or_file_io = weak_scaling_profiling_data[\"t_total\"] \\\n",
@@ -294,13 +413,14 @@
     "#ax_weak.plot(weak_scaling_profiling_data[\"n_processes\"].to_numpy(dtype=\"int\"), \n",
     "#    speedup(t_total_no_init_or_file_io[0], t_total_no_init_or_file_io), label=\"Total (no init or file I/O)\")\n",
     "\n",
-    "ax_weak.plot(weak_scaling_profiling_data[\"n_processes\"][0:].to_numpy(dtype=\"int\"), \n",
-    "    speedup(weak_scaling_profiling_data[\"t_full_step\"][0], weak_scaling_profiling_data[\"t_full_step\"][0:]), label=\"Total (no init or file I/O)\")\n",
+    "ax_weak.plot(weak_scaling_profiling_data[\"n_processes\"].to_numpy(dtype=\"int\"), \n",
+    "    speedup(weak_scaling_profiling_data[\"t_full_step\"][0], weak_scaling_profiling_data[\"t_full_step\"]), label=\"Total runtime (except init and file I/O)\")\n",
+    "ax_weak.locator_params(axis=\"x\", nbins=16)\n",
     "\n",
+    "\"\"\"\n",
     "ax_weak.plot(weak_scaling_profiling_data[\"n_processes\"][0:].to_numpy(dtype=\"int\"), \n",
     "    speedup(t_total_halo_exchange[0], t_total_halo_exchange[0:]), label=\"Halo exchange (D/E/U)\", linestyle=\"dashed\")\n",
     "\n",
-    "\"\"\"\n",
     "ax_weak.plot(weak_scaling_profiling_data[\"n_processes\"].to_numpy(dtype=\"int\"), \n",
     "    speedup(weak_scaling_profiling_data[\"t_total\"][0], weak_scaling_profiling_data[\"t_total\"]), label=\"Total\")\n",
     "\n",
@@ -324,7 +444,7 @@
     "\n",
     "ax_weak.set_xlabel(\"Number of ranks/GPUs\")\n",
     "ax_weak.set_ylabel(\"Efficiency\")\n",
-    "ax_weak.legend(loc=\"lower right\")\n",
+    "ax_weak.legend(loc=\"upper right\", bbox_to_anchor=[1.0, 0.95])\n",
     "#fig.show()\n",
     "\n",
     "##############################################\n",
@@ -344,12 +464,12 @@
     "#    speedup(t_total_no_init_or_file_io[0], t_total_no_init_or_file_io)*4, label=\"Total (no init or file I/O)\")\n",
     "\n",
     "ax_strong.plot(strong_scaling_profiling_data[\"n_processes\"].to_numpy(dtype=\"int\"), \n",
-    "    speedup(strong_scaling_profiling_data[\"t_full_step\"][0], strong_scaling_profiling_data[\"t_full_step\"])*4, label=\"Total (no init or file I/O)\")\n",
+    "    speedup(strong_scaling_profiling_data[\"t_full_step\"][0], strong_scaling_profiling_data[\"t_full_step\"])*4, label=\"Total runtime (except init and file I/O)\")\n",
     "\n",
+    "\"\"\"\n",
     "ax_strong.plot(strong_scaling_profiling_data[\"n_processes\"].to_numpy(dtype=\"int\"), \n",
     "    speedup(t_total_halo_exchange[0], t_total_halo_exchange)*4, label=\"Halo exchange (D/E/U)\", linestyle=\"dashed\")\n",
     "\n",
-    "\"\"\"\n",
     "ax_strong.plot(strong_scaling_profiling_data[\"n_processes\"].to_numpy(dtype=\"int\"), \n",
     "    speedup(strong_scaling_profiling_data[\"t_total\"][0], strong_scaling_profiling_data[\"t_total\"])*4, label=\"Total\")\n",
     "    \n",
@@ -382,7 +502,9 @@
     "ax_strong.set_xlabel(\"Number of ranks/GPUs\")\n",
     "ax_strong.set_ylabel(\"Speedup\")\n",
     "ax_strong.legend(loc=\"upper left\")\n",
-    "fig.show()"
+    "fig.show()\n",
+    "\n",
+    "fig.savefig(\"dgx-2-scaling.pdf\", bbox_inches='tight')"
    ]
   },
   {
diff --git a/dgx-2_scaling_benchmark.job b/dgx-2_scaling_benchmark.job
index fce0443..d4c7cb5 100644
--- a/dgx-2_scaling_benchmark.job
+++ b/dgx-2_scaling_benchmark.job
@@ -6,7 +6,7 @@
 #SBATCH -t 0-00:10                 # time (D-HH:MM)
 #SBATCH -o slurm.%N.%j.out  # STDOUT
 #SBATCH -e slurm.%N.%j.err  # STDERR
-#SBATCH --reservation=martinls_8
+#SBATCH --reservation=martinls_17
 
 
 # For Linux 64, Open MPI is built with CUDA awareness but this support is disabled by default.
diff --git a/dgx-2_strong_scaling_benchmark.sh b/dgx-2_strong_scaling_benchmark.sh
index 7414a7b..cf6121d 100644
--- a/dgx-2_strong_scaling_benchmark.sh
+++ b/dgx-2_strong_scaling_benchmark.sh
@@ -22,17 +22,52 @@ TIMESTAMP=$(date "+%Y-%m-%dT%H%M%S")
 #sbatch --nodes=1 --gpus-per-node=16 --ntasks-per-node=16 --export=ALL,NX=8192,NY=512,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
 
 # one node: 4-16 GPUs
-sbatch --nodes=1 --gpus-per-node=4 --ntasks-per-node=4 --export=ALL,NX=41984,NY=10496,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=5 --ntasks-per-node=5 --export=ALL,NX=41984,NY=8396,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=6 --ntasks-per-node=6 --export=ALL,NX=41984,NY=6997,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=7 --ntasks-per-node=7 --export=ALL,NX=41984,NY=5997,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=8 --ntasks-per-node=8 --export=ALL,NX=41984,NY=5248,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+#sbatch --nodes=1 --gpus-per-node=4 --ntasks-per-node=4 --export=ALL,NX=41984,NY=10496,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+#sbatch --nodes=1 --gpus-per-node=5 --ntasks-per-node=5 --export=ALL,NX=41984,NY=8396,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+#sbatch --nodes=1 --gpus-per-node=6 --ntasks-per-node=6 --export=ALL,NX=41984,NY=6997,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+#sbatch --nodes=1 --gpus-per-node=7 --ntasks-per-node=7 --export=ALL,NX=41984,NY=5997,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+#sbatch --nodes=1 --gpus-per-node=8 --ntasks-per-node=8 --export=ALL,NX=41984,NY=5248,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+#
+#sbatch --nodes=1 --gpus-per-node=9 --ntasks-per-node=9 --export=ALL,NX=41984,NY=4664,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+#sbatch --nodes=1 --gpus-per-node=10 --ntasks-per-node=10 --export=ALL,NX=41984,NY=4198,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+#sbatch --nodes=1 --gpus-per-node=11 --ntasks-per-node=11 --export=ALL,NX=41984,NY=3816,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+#sbatch --nodes=1 --gpus-per-node=12 --ntasks-per-node=12 --export=ALL,NX=41984,NY=3498,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+#sbatch --nodes=1 --gpus-per-node=13 --ntasks-per-node=13 --export=ALL,NX=41984,NY=3229,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+#sbatch --nodes=1 --gpus-per-node=14 --ntasks-per-node=14 --export=ALL,NX=41984,NY=2998,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+#sbatch --nodes=1 --gpus-per-node=15 --ntasks-per-node=15 --export=ALL,NX=41984,NY=2798,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+#sbatch --nodes=1 --gpus-per-node=16 --ntasks-per-node=16 --export=ALL,NX=41984,NY=2624,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
 
-sbatch --nodes=1 --gpus-per-node=9 --ntasks-per-node=9 --export=ALL,NX=41984,NY=4664,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=10 --ntasks-per-node=10 --export=ALL,NX=41984,NY=4198,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=11 --ntasks-per-node=11 --export=ALL,NX=41984,NY=3816,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=12 --ntasks-per-node=12 --export=ALL,NX=41984,NY=3498,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=13 --ntasks-per-node=13 --export=ALL,NX=41984,NY=3229,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=14 --ntasks-per-node=14 --export=ALL,NX=41984,NY=2998,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=15 --ntasks-per-node=15 --export=ALL,NX=41984,NY=2798,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=16 --ntasks-per-node=16 --export=ALL,NX=41984,NY=2624,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+# one node: 1-16 GPUs
+sbatch --nodes=1 --gpus-per-node=1 --ntasks-per-node=1 --export=ALL,NX=22528,NY=22528,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=2 --ntasks-per-node=2 --export=ALL,NX=22528,NY=11264,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=3 --ntasks-per-node=3 --export=ALL,NX=22528,NY=7509,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=4 --ntasks-per-node=4 --export=ALL,NX=22528,NY=5632,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=5 --ntasks-per-node=5 --export=ALL,NX=22528,NY=4505,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=6 --ntasks-per-node=6 --export=ALL,NX=22528,NY=3754,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=7 --ntasks-per-node=7 --export=ALL,NX=22528,NY=3218,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=8 --ntasks-per-node=8 --export=ALL,NX=22528,NY=2816,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+
+sbatch --nodes=1 --gpus-per-node=9 --ntasks-per-node=9 --export=ALL,NX=22528,NY=2503,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=10 --ntasks-per-node=10 --export=ALL,NX=22528,NY=2252,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=11 --ntasks-per-node=11 --export=ALL,NX=22528,NY=2048,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=12 --ntasks-per-node=12 --export=ALL,NX=22528,NY=1877,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=13 --ntasks-per-node=13 --export=ALL,NX=22528,NY=1732,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=14 --ntasks-per-node=14 --export=ALL,NX=22528,NY=1609,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=15 --ntasks-per-node=15 --export=ALL,NX=22528,NY=1501,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=16 --ntasks-per-node=16 --export=ALL,NX=22528,NY=1408,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+
+# one node: 4-16 GPUs
+sbatch --nodes=1 --gpus-per-node=4 --ntasks-per-node=4 --export=ALL,NX=45056,NY=11264,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=5 --ntasks-per-node=5 --export=ALL,NX=45056,NY=8396,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=6 --ntasks-per-node=6 --export=ALL,NX=45056,NY=6997,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=7 --ntasks-per-node=7 --export=ALL,NX=45056,NY=5997,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=8 --ntasks-per-node=8 --export=ALL,NX=45056,NY=5248,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+
+sbatch --nodes=1 --gpus-per-node=9 --ntasks-per-node=9 --export=ALL,NX=45056,NY=4664,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=10 --ntasks-per-node=10 --export=ALL,NX=45056,NY=4198,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=11 --ntasks-per-node=11 --export=ALL,NX=45056,NY=3816,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=12 --ntasks-per-node=12 --export=ALL,NX=45056,NY=3498,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=13 --ntasks-per-node=13 --export=ALL,NX=45056,NY=3229,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=14 --ntasks-per-node=14 --export=ALL,NX=45056,NY=2998,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=15 --ntasks-per-node=15 --export=ALL,NX=45056,NY=2798,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=16 --ntasks-per-node=16 --export=ALL,NX=45056,NY=2624,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
diff --git a/dgx-2_weak_scaling_benchmark.sh b/dgx-2_weak_scaling_benchmark.sh
index a24ee65..fddabf9 100644
--- a/dgx-2_weak_scaling_benchmark.sh
+++ b/dgx-2_weak_scaling_benchmark.sh
@@ -22,20 +22,20 @@ TIMESTAMP=$(date "+%Y-%m-%dT%H%M%S")
 #sbatch --nodes=1 --gpus-per-node=16 --ntasks-per-node=16 --export=ALL,NX=8192,NY=8192,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
 
 # one node: 1-16 GPUs
-sbatch --nodes=1 --gpus-per-node=1 --ntasks-per-node=1 --export=ALL,NX=41984,NY=41984,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=2 --ntasks-per-node=2 --export=ALL,NX=41984,NY=41984,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=3 --ntasks-per-node=3 --export=ALL,NX=41984,NY=41984,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=4 --ntasks-per-node=4 --export=ALL,NX=41984,NY=41984,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=5 --ntasks-per-node=5 --export=ALL,NX=41984,NY=41984,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=6 --ntasks-per-node=6 --export=ALL,NX=41984,NY=41984,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=7 --ntasks-per-node=7 --export=ALL,NX=41984,NY=41984,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=8 --ntasks-per-node=8 --export=ALL,NX=41984,NY=41984,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=1 --ntasks-per-node=1 --export=ALL,NX=22528,NY=22528,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=2 --ntasks-per-node=2 --export=ALL,NX=22528,NY=22528,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=3 --ntasks-per-node=3 --export=ALL,NX=22528,NY=22528,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=4 --ntasks-per-node=4 --export=ALL,NX=22528,NY=22528,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=5 --ntasks-per-node=5 --export=ALL,NX=22528,NY=22528,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=6 --ntasks-per-node=6 --export=ALL,NX=22528,NY=22528,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=7 --ntasks-per-node=7 --export=ALL,NX=22528,NY=22528,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=8 --ntasks-per-node=8 --export=ALL,NX=22528,NY=22528,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
 
-sbatch --nodes=1 --gpus-per-node=9 --ntasks-per-node=9 --export=ALL,NX=41984,NY=41984,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=10 --ntasks-per-node=10 --export=ALL,NX=41984,NY=41984,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=11 --ntasks-per-node=11 --export=ALL,NX=41984,NY=41984,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=12 --ntasks-per-node=12 --export=ALL,NX=41984,NY=41984,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=13 --ntasks-per-node=13 --export=ALL,NX=41984,NY=41984,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=14 --ntasks-per-node=14 --export=ALL,NX=41984,NY=41984,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=15 --ntasks-per-node=15 --export=ALL,NX=41984,NY=41984,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
-sbatch --nodes=1 --gpus-per-node=16 --ntasks-per-node=16 --export=ALL,NX=41984,NY=41984,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
\ No newline at end of file
+sbatch --nodes=1 --gpus-per-node=9 --ntasks-per-node=9 --export=ALL,NX=22528,NY=22528,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=10 --ntasks-per-node=10 --export=ALL,NX=22528,NY=22528,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=11 --ntasks-per-node=11 --export=ALL,NX=22528,NY=22528,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=12 --ntasks-per-node=12 --export=ALL,NX=22528,NY=22528,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=13 --ntasks-per-node=13 --export=ALL,NX=22528,NY=22528,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=14 --ntasks-per-node=14 --export=ALL,NX=22528,NY=22528,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=15 --ntasks-per-node=15 --export=ALL,NX=22528,NY=22528,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
+sbatch --nodes=1 --gpus-per-node=16 --ntasks-per-node=16 --export=ALL,NX=22528,NY=22528,NOW=$TIMESTAMP dgx-2_scaling_benchmark.job
\ No newline at end of file