FiniteVolumeGPU/TestSchemes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets have matplotlib \"inline\"\n",
    "%matplotlib inline\n",
    "\n",
    "# Add line profiler\n",
    "%load_ext line_profiler\n",
    "\n",
    "#Import packages we need\n",
    "import numpy as np\n",
    "from matplotlib import animation, rc\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import subprocess\n",
    "import os\n",
    "import gc\n",
    "import datetime\n",
    "import importlib\n",
    "import logging\n",
    "\n",
    "import pycuda.driver as cuda\n",
    "import pycuda.compiler\n",
    "\n",
    "try:\n",
    "    from StringIO import StringIO\n",
    "except ImportError:\n",
    "    from io import StringIO\n",
    "    \n",
    "#Set large figure sizes\n",
    "#Note, this prevents nice figures for articles...\n",
    "rc('figure', figsize=(16.0, 12.0))\n",
    "rc('animation', html='html5')\n",
    "\n",
    "from GPUSimulators import Common, IPythonMagic\n",
    "from GPUSimulators.helpers import InitialConditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Console logger using level INFO\n",
      "File logger using level DEBUG to test_schemes.log\n",
      "Python version 3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) \n",
      "[GCC 12.3.0]\n"
     ]
    }
   ],
   "source": [
    "%setup_logging --out=test_schemes.log TestSchemes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Registering my_context in user workspace\n",
      "PyCUDA version 2024.1\n",
      "CUDA version (11, 8, 0)\n",
      "Driver version 12080\n",
      "Using device 0/1 'NVIDIA GeForce GTX 970' (0000:07:00.0) GPU\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Created context handle <98151551869168>\n",
      "Using CUDA cache dir /home/smyalygames/PycharmProjects/FiniteVolumeGPU/GPUSimulators/cuda_cache\n",
      "Autotuning enabled. It may take several minutes to run the code the first time: have patience\n"
     ]
    }
   ],
   "source": [
    "%cuda_context_handler my_context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx = 256\n",
    "ny = 128\n",
    "g = 9.81\n",
    "\n",
    "h0, hu0, hv0, dx, dy = InitialConditions.bump(nx, ny, 100, 100, 15)\n",
    "\n",
    "# print(my_context.autotuner.get)\n",
    "\n",
    "arguments = {\n",
    "    'context': my_context,\n",
    "    'h0': h0, 'hu0': hu0, 'hv0': hv0,\n",
    "    'nx': nx, 'ny': ny,\n",
    "    'dx': dx, 'dy': dy, \n",
    "    'g': g,\n",
    "    'compile_opts': ['-Wno-deprecated-gpu-targets', '-arch=sm_52']\n",
    "}\n",
    "\n",
    "t_end = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(sim):\n",
    "    h = sim.u0[0].download(sim.stream)\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.title(str(sim) + \", t=\" + str(sim.simTime()) + \", nt=\" + str(sim.simSteps()))\n",
    "    extent = [0, sim.dx*sim.nx, 0, sim.dy*sim.ny]\n",
    "    plt.imshow(h, vmin=0.49, vmax=0.52, extent=extent)\n",
    "    plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from GPUSimulators import LxF\n",
    "importlib.reload(LxF)\n",
    "\n",
    "with Common.Timer(\"construct\") as t:\n",
    "    sim = LxF.LxF(**arguments)\n",
    "\n",
    "with Common.Timer(\"step\") as t:\n",
    "    t = sim.simulate(t_end)\n",
    "    \n",
    "with Common.Timer(\"download\") as t:\n",
    "    h1, hu1, hv1 = sim.download()\n",
    "\n",
    "plot(sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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++mn5wAc+ILvvvvvYv//3//6f/OIXv5Ddd99dzj333B5sFQAA6DXuyAGAHczBBx8sK1askL/7u7+TU045RV7xilcE5d/97nflf/7P/yl/+Id/KMuWLTPbWrRokZxzzjlywQUXyBe/+EV561vfKqeffrp87GMfk3POOUeOOOKIsefL+L70pS/Jm970plq3ayLXXXednHHGGfKOd7xDPvvZz1a6iPP9739f7r333uAumkMPPVRe9KIXyS233CLtdnvsrpwHH3xQfvKTn8jb3va2oI33vOc9smHDBvnBD34gv/zlL+XEE0+Uj3/84/IXf/EXHW3Phg0bxhKv1q9fL3vttdeE9RYsWCDf+ta35IEHHgj2//YH6Za5Q6YK606TTr9aVXZb3/CGN8iHP/xhuf766+WDH/zg2O+vu+46mTVrlvzhH/6h2e8f/dEfyWc+8xlZu3atnHzyyWO/v/7662XBggVy6KGHFo69G0NDQ7l36PTyq1UHHXSQ3HrrrdHvV65cKY899phce+21tR8nAABgcnAhBwB2QDfccIMce+yxsmLFCnn/+98/djfEP//zP8snPvEJeelLXyrXXXddqbbOPvts+fSnPy0XXHCBnHTSSbLrrrvKP/zDP8hrX/taWbp0qbzvfe+T5cuXy4wZM+Tee++VG2+8UX7yk59EF3LuvPPO6Bk5IqNfL5kzZ07lbfz7v/97Of300+Wggw6Sd7/73fKDH/wgKF+6dOnYBYiXv/zl8o53vEP233//sYcdf+xjH5N58+bJOeecM7ZOmqby8Y9/XE466SR5wxveIH/2Z38mTz75pFx00UUyY8aM4A6Gz372s3LjjTfKtddeK4sXL5bFixfL+973PvngBz8or3jFK8aitEVE/vf//t8iMnqXhMhoRPz2BKHtUe2bN2+Wo48+Wh566CH5u7/7O9m8ebNs3rx5rI0XvehFY394v/e975XPf/7zctxxx8mHPvShsWfkXHzxxTJ37lx5+9vfXmof/smf/Ilcf/31ct9990UPMfYtWbJEREQ+8YlPyKmnniqDg4Oy3377yS677CILFy40151IlW1dvHixnH766XL++edLq9WSQw45RG655Ra55ppr5OKLLw6+GnXhhRfKhRdeKN/61rfkyCOPFBGR448/Xo477jj5sz/7M9myZYvsu+++cvPNN8vXv/51ufHGG4Ov0F133XXyrne9S6699lr5kz/5k0rblGfJkiXypS99Sa6++mpZtmyZpGk69rXI7fu1qh/+8IdjX4/csmWLOOfGjrFDDjlE9tprL9ltt93kqKOOitbdbbfdZGRkJCq7//775Y477hARkX//938XkfHjduHChblf5QQAAFPAAQAa49prr3Ui4u64447Cuk888YRbvXq1O+igg9xOO+3kdtppJ/eyl73MXXzxxe6JJ56I6u+1117uNa95zYRt/Y//8T+ciLjrr79+7HebNm1yH/zgB93ixYvdTjvt5IaGhty+++7r3v3ud7uf/vSnY/XOP/98JyK5/9atW2duh4i4888/P/r9qaeearZ73333jdV9y1ve4vbdd183e/ZsNzg46Pbaay/3nve8xz344IMT9vmVr3zFHXLIIW7mzJlu1113da9//evd3XffPVb+r//6r27WrFnu1FNPDdZ75pln3LJly9zChQvdo48+GmxD3r/tbr31VrOe3gc/+tGP3B/90R+5F73oRW5oaMi9+MUvdmeccYbbsGHDhNt06qmnutmzZwe/e/Ob3+xmzZoVjDXPueee6xYsWODSNHUi4m699dbCdfJU3dbh4WF3/vnnuz333NPNmDHDveQlL3H//b//96jd7ceaHtvjjz/u3v/+97t58+a5GTNmuJe97GXu5ptvjtb/5Cc/6UTEff3rXy81/r//+78Pfn/fffc5EXHXXnvt2O9+97vfuRNPPNHttttuLkmS4DXvlHXs+31P5Mgjj3SLFy+Ofr/9s2Wif/o4BwAAUytxrsRT9gAAmCJJkshHPvIR+Zu/+RtptVpdpQxNR1mWSZZlcvrpp8vatWvliSeeGCubN2+enHLKKaUfEL2jO+mkk+S+++4buzMFAACgH/GwYwBA37voootkcHBQ/vZv/3aqh9I4Z511lgwODsoNN9wQ/P7uu++Wp556KnjuzHTmnJP169fLJZdcMtVDAQAAMHFHDgCgr/3whz8c+3mPPfaQuXPnTuFommfjxo3y29/+VkREWq2WLF26dIpHBAAAgG5wIQcAAAAAAKAhKn+16jvf+Y687nWvkwULFkiSJPKVr3wlKHfOyapVq2TBggUya9YsOeqoo+Tuu+8O6mzdulX+/M//XJ73vOfJ7Nmz5fWvf7385je/6WpDAAAAAAAAyvjUpz4le++9t8ycOVOWLVsmt912W27d9evXS5Ik0b9f/vKXY3U+85nPyBFHHCG777677L777nLsscdGqapV+81T+ULOk08+KS9/+cvlqquumrD88ssvlyuvvFKuuuoqueOOO2TevHly3HHHyeOPPz5WZ+XKlfLlL39ZvvCFL8h3v/tdeeKJJ+S1r32ttNvtyhsAAAAAAABQ1he/+EVZuXKlnHfeeXLXXXfJEUccIccff7xs2LDBXO+ee+6Rhx56aOzfokWLxsrWr18vb33rW+XWW2+V733ve7LnnnvKihUr5IEHHui6X62rr1YlSSJf/vKX5Y1vfKOIjN6Ns2DBAlm5cuXYwxO3bt0qc+fOlcsuu0ze/e53y2OPPSbPf/7z5XOf+5ycfPLJIiLy4IMPyh577CFf+9rX5NWvfnWnwwEAAAAAADAdeuih8nu/93ty9dVXj/1u//33lze+8Y1y6aWXRvXXr18vRx99tDz66KOy2267leqj3W7L7rvvLldddZW8853v7KjfPAOla5Zw3333yaZNm2TFihVjvxsaGpIjjzxSbr/9dnn3u98td955p2zbti2os2DBAjnwwAPl9ttvn/BCztatW2Xr1q1jy1mWye9+9zt57nOfSwwtAAAAAKA2zjl5/PHHZcGCBZKm0yvo+ZlnnpHh4eGpHkZHnHPR9YGhoSEZGhoKfjc8PCx33nmnfOhDHwp+v2LFCrn99tvNPpYuXSrPPPOMHHDAAfLhD39Yjj766Ny6Tz31lGzbtk2e85zndN2vVuuFnE2bNomIRIkic+fOlfvvv3+szowZM2T33XeP6mxfX7v00kvlggsuqHOoAAAAAADk2rhxo7zoRS+a6mFMmmeeeUb23mtn2bS5mY882XnnneWJJ54Ifnf++efLqlWrgt89/PDD0m63J7xukXdNYv78+XLNNdfIsmXLZOvWrfK5z31OjjnmGFm/fr286lWvmnCdD33oQ/LCF75Qjj322I77zVPrhZzt9FWwia6MaVadc889V84666yx5ccee0z23HNPOeDtH5HWjJndD3iy1XQTkavSToW6znqtkpyfJ2rH6N8cuy7zlp26IB61Y4zPGe1YY4jq+mW6j6iutxf02P3lov2TWmXhtyOdVdccj/qWpbWP/LoF4/GXo0PLLwuPGEmMfRltVprl95Godr3yNM0vG112E/6sx5BGfdjLvjTJr5dK/npV+rDebnrsZcuKxtNpW1X6jNY19pelaOxmnx2u283NpJ32WWX/pElWXGnC9ar00aO6lbaz7HHZ2f4Q6fy47HdZhYlFZpx0M3Wistr160brRcvjfeo2dV3/IQPtLByrv65eT9cd8Zb1q555ZW21P9pZfrvRdnl1M7WeM5ajbW6PLztdptoRr67osmh5/MfEaEe/9fRbLAn6VGX+FES/NaNlr0+jnagPq641dl1Woa65ri4TQ0G7YR9GYYWPri5Oox332Wvt4Wfk55+/SHbZZZepHsqkGh4elk2b23L/nQtlzi7NuhNpy+OZ7LXsP2Tjxo0yZ86csd/ru3F8Va5b7LfffrLffvuNLS9fvlw2btwoV1xxxYQXci6//HK5+eabZf369TJzZnjNopPrJVqtF3LmzZsnIqN33cyfP3/s95s3bx676jRv3jwZHh6WRx99NLgrZ/PmzXL44YdP2O5Et0OJiLRmzORCTg/6rO1CTsmLKmYfarnvLuRYF2dEgjObWbdXF3IqXVia4gs5+sJETRdy4gseXvdpllum152SCznWxZkqdXNLuJAjMjUXcrraTi7k2HX77EJOq7a/cPpLu8IkpN8v5PjLSYULObquGBdygos+0Xr6Ysh4eRL1aYzVurBjXcgpuCDUkws5RRdOarqQk1gXcvzlpl3IsT5WuJBTq+n6GI85u6QyZ5fWVA+jI3PmzAku5Ezkec97nrRareguGP+6RRmHHXaY3HjjjdHvr7jiClm9erV885vflJe97GW19yvSQWqVZe+995Z58+bJunXrxn43PDws3/72t8cu0ixbtkwGBweDOg899JD87Gc/y72QAwAAAAAA0K0ZM2bIsmXLgmsSIiLr1q2rdE3irrvuCm5gERH52Mc+JhdddJF8/etfl4MPPrgn/Yp0cEfOE088Ib/61a/Glu+77z758Y9/LM95znNkzz33lJUrV8rq1atl0aJFsmjRIlm9erXstNNO8ra3vU1ERHbddVc5/fTT5S//8i/luc99rjznOc+Rs88+W5YsWTL23TEAAAAAADD5MnGSRd9V7G9ZxVu6zjrrLDnllFPk4IMPluXLl8s111wjGzZskPe85z0iMvp4lwceeEBuuOEGERFZs2aNLFy4UBYvXizDw8Ny4403ytq1a2Xt2rVjbV5++eXykY98RG666SZZuHDh2J03O++8s+y8886l+i2r8oWcH/7wh8GTmbc/u+bUU0+V6667Ts455xx5+umn5cwzz5RHH31UDj30ULnllluC7xd+/OMfl4GBATnppJPk6aeflmOOOUauu+46abWaefsWAAAAAABohpNPPlkeeeQRufDCC+Whhx6SAw88UL72ta/JXnvtJSKj3xrasGHDWP3h4WE5++yz5YEHHpBZs2bJ4sWL5atf/aqccMIJY3U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      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from GPUSimulators import FORCE\n",
    "importlib.reload(FORCE)\n",
    "\n",
    "with Common.Timer(\"construct\") as t:\n",
    "    sim = FORCE.FORCE(**arguments)\n",
    "\n",
    "with Common.Timer(\"step\") as t:\n",
    "    t = sim.simulate(t_end)\n",
    "    \n",
    "with Common.Timer(\"download\") as t:\n",
    "    h1, hu1, hv1 = sim.download()\n",
    "\n",
    "plot(sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from GPUSimulators import HLL\n",
    "importlib.reload(HLL)\n",
    "\n",
    "with Common.Timer(\"construct\") as t:\n",
    "    sim = HLL.HLL(**arguments)\n",
    "\n",
    "with Common.Timer(\"step\") as t:\n",
    "    t = sim.simulate(t_end)\n",
    "    \n",
    "with Common.Timer(\"download\") as t:\n",
    "    h1, hu1, hv1 = sim.download()\n",
    "\n",
    "plot(sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from GPUSimulators import HLL2\n",
    "importlib.reload(HLL2)\n",
    "\n",
    "with Common.Timer(\"construct\") as t:\n",
    "    sim = HLL2.HLL2(**arguments)\n",
    "\n",
    "with Common.Timer(\"step\") as t:\n",
    "    t = sim.simulate(t_end)\n",
    "    \n",
    "with Common.Timer(\"download\") as t:\n",
    "    h1, hu1, hv1 = sim.download()\n",
    "\n",
    "plot(sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Benchmarking KP07 to autotuning_data_archvm.npz\n",
      "PyCUDA version 2024.1\n",
      "CUDA version (11, 8, 0)\n",
      "Driver version 12080\n",
      "Using device 0/1 'NVIDIA GeForce GTX 970' (0000:07:00.0) GPU\n",
      "Created context handle <98151567013168>\n",
      "Using CUDA cache dir /home/smyalygames/PycharmProjects/FiniteVolumeGPU/GPUSimulators/cuda_cache\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m importlib\u001b[38;5;241m.\u001b[39mreload(KP07)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m Common\u001b[38;5;241m.\u001b[39mTimer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconstruct\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[0;32m----> 5\u001b[0m     sim \u001b[38;5;241m=\u001b[39m \u001b[43mKP07\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mKP07\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43marguments\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m Common\u001b[38;5;241m.\u001b[39mTimer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstep\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m      8\u001b[0m     t \u001b[38;5;241m=\u001b[39m sim\u001b[38;5;241m.\u001b[39msimulate(t_end)\n",
      "File \u001b[0;32m~/PycharmProjects/FiniteVolumeGPU/GPUSimulators/KP07.py:70\u001b[0m, in \u001b[0;36mKP07.__init__\u001b[0;34m(self, context, h0, hu0, hv0, nx, ny, dx, dy, g, theta, cfl_scale, order, boundary_conditions, block_width, block_height, dt, compile_opts)\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     54\u001b[0m \u001b[38;5;124;03mInitialization routine\u001b[39;00m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;124;03m\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;124;03m    compile_opts: Pass a list of nvcc compiler options\u001b[39;00m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     69\u001b[0m \u001b[38;5;66;03m# Call super constructor\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     71\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mny\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     72\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[43m    \u001b[49m\u001b[43mboundary_conditions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     74\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcfl_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     75\u001b[0m \u001b[43m    \u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     76\u001b[0m \u001b[43m    \u001b[49m\u001b[43mblock_width\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mblock_height\u001b[49m\u001b[43m)\u001b[49m;\n\u001b[1;32m     77\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mg \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mfloat32(g)             \n\u001b[1;32m     78\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtheta \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mfloat32(theta) \n",
      "File \u001b[0;32m~/PycharmProjects/FiniteVolumeGPU/GPUSimulators/Simulator.py:146\u001b[0m, in \u001b[0;36mBaseSimulator.__init__\u001b[0;34m(self, context, nx, ny, dx, dy, boundary_conditions, cfl_scale, num_substeps, block_width, block_height)\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[38;5;66;03m#Handle autotuning block size\u001b[39;00m\n\u001b[1;32m    145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontext\u001b[38;5;241m.\u001b[39mautotuner:\n\u001b[0;32m--> 146\u001b[0m     peak_configuration \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautotuner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_peak_performance\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__class__\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    147\u001b[0m     block_width \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(peak_configuration[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mblock_width\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m    148\u001b[0m     block_height \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(peak_configuration[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mblock_height\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n",
      "File \u001b[0;32m~/PycharmProjects/FiniteVolumeGPU/GPUSimulators/Autotuner.py:121\u001b[0m, in \u001b[0;36mAutotuner.get_peak_performance\u001b[0;34m(self, simulator)\u001b[0m\n\u001b[1;32m    119\u001b[0m     logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not get autotuned peak performance for \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m: benchmarking\u001b[39m\u001b[38;5;124m\"\u001b[39m, key)\n\u001b[1;32m    120\u001b[0m     data\u001b[38;5;241m.\u001b[39mclose()\n\u001b[0;32m--> 121\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbenchmark\u001b[49m\u001b[43m(\u001b[49m\u001b[43msimulator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    122\u001b[0m     data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilename)\n\u001b[1;32m    124\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfind_max_index\u001b[39m(megacells):\n",
      "File \u001b[0;32m~/PycharmProjects/FiniteVolumeGPU/GPUSimulators/Autotuner.py:84\u001b[0m, in \u001b[0;36mAutotuner.benchmark\u001b[0;34m(self, simulator, force)\u001b[0m\n\u001b[1;32m     81\u001b[0m             benchmark_data[k] \u001b[38;5;241m=\u001b[39m v\n\u001b[1;32m     83\u001b[0m \u001b[38;5;66;03m# Run benchmark\u001b[39;00m\n\u001b[0;32m---> 84\u001b[0m benchmark_data[key \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_megacells\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mAutotuner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbenchmark_single_simulator\u001b[49m\u001b[43m(\u001b[49m\u001b[43msimulator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43marguments\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mblock_widths\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mblock_heights\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     85\u001b[0m benchmark_data[key \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_block_widths\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblock_widths\n\u001b[1;32m     86\u001b[0m benchmark_data[key \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_block_heights\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblock_heights\n",
      "File \u001b[0;32m~/PycharmProjects/FiniteVolumeGPU/GPUSimulators/Autotuner.py:162\u001b[0m, in \u001b[0;36mAutotuner.benchmark_single_simulator\u001b[0;34m(simulator, arguments, block_widths, block_heights)\u001b[0m\n\u001b[1;32m    160\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i, block_width \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(block_widths):\n\u001b[1;32m    161\u001b[0m             sim_arguments\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblock_width\u001b[39m\u001b[38;5;124m'\u001b[39m: block_width})\n\u001b[0;32m--> 162\u001b[0m             megacells[j, i] \u001b[38;5;241m=\u001b[39m \u001b[43mAutotuner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_benchmark\u001b[49m\u001b[43m(\u001b[49m\u001b[43msimulator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msim_arguments\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    165\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCompleted \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m in \u001b[39m\u001b[38;5;132;01m%f\u001b[39;00m\u001b[38;5;124m seconds\u001b[39m\u001b[38;5;124m\"\u001b[39m, simulator\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, t\u001b[38;5;241m.\u001b[39msecs)\n\u001b[1;32m    167\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m megacells\n",
      "File \u001b[0;32m~/PycharmProjects/FiniteVolumeGPU/GPUSimulators/Autotuner.py:200\u001b[0m, in \u001b[0;36mAutotuner.run_benchmark\u001b[0;34m(simulator, arguments, timesteps, warmup_timesteps)\u001b[0m\n\u001b[1;32m    197\u001b[0m end\u001b[38;5;241m.\u001b[39mrecord(sim\u001b[38;5;241m.\u001b[39mstream)\n\u001b[1;32m    199\u001b[0m \u001b[38;5;66;03m#Synchronize end event\u001b[39;00m\n\u001b[0;32m--> 200\u001b[0m \u001b[43mend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msynchronize\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    202\u001b[0m \u001b[38;5;66;03m#Compute megacells\u001b[39;00m\n\u001b[1;32m    203\u001b[0m gpu_elapsed \u001b[38;5;241m=\u001b[39m end\u001b[38;5;241m.\u001b[39mtime_since(start)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m1.0e-3\u001b[39m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from GPUSimulators import KP07\n",
    "importlib.reload(KP07)\n",
    "\n",
    "with Common.Timer(\"construct\") as t:\n",
    "    sim = KP07.KP07(**arguments)\n",
    "\n",
    "with Common.Timer(\"step\") as t:\n",
    "    t = sim.simulate(t_end)\n",
    "    \n",
    "with Common.Timer(\"download\") as t:\n",
    "    h1, hu1, hv1 = sim.download()\n",
    "\n",
    "plot(sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Benchmarking KP07_dimsplit to autotuning_data_archvm.npz\n",
      "PyCUDA version 2024.1\n",
      "CUDA version (11, 8, 0)\n",
      "Driver version 12080\n",
      "Using device 0/1 'NVIDIA GeForce GTX 970' (0000:07:00.0) GPU\n",
      "Created context handle <100365813949744>\n",
      "Using CUDA cache dir /home/smyalygames/PycharmProjects/FiniteVolumeGPU/GPUSimulators/cuda_cache\n",
      "Cleaning up CUDA context handle <100365813949744>\n"
     ]
    },
    {
     "data": {
      "image/png": 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2OWI8uVzQNX+s8cvqE4Ip5hdFjKtIcT/X0vetD7I+flxHjkdx40s2NjwMdfy4qy0uqX4ubny7ce/7Wac+acu6ibhxEZFur6713Mme6hvHjTfHj1c+frxXt0sfN+7PsTs6NtyfizZHisd99Ziub8+1Vax45SLFdeR46Lio+66LBlftTtdFindGq94WEekXtj1T1PtlJ7fjdLO6XbgXXOkOwMPNel/GhsrCmPfubaht810vPpEDAAAAAADQElzIAQAAAAAAaAku5AAAAAAAALTEWfkbOQAAAAAAYA2CtC9+vG3zXSc+kQMAAAAAANASXMgBAAAAAABoiVZ/tWoYypUIu2GoY+903LiIyGkVwbzocnkXQ8e161zARZchqCPHffy4j8wufeax4uPHdTvPXWRgXs+3KHzUposYVw8l+PxhE03eODUR8dHg9nEEM46PP3cDhYbbIjYTetyEVN84UtVHlU82js9ndpvdbQMXL5pYnY8mDeo5i/YIn7uuxyltrKOZri3FJo51dPuPH1fHj1duA6kYzCg6PRVH7l43ego+MtDvX2afdru3XkfIfd54c5x99KS4ZXU8eZbIr960aPJo7smB6ptTRByvK6q8Yf2rljcobnTiGPNowQ1ZPbbIdosR99YcK75RkeLrGHejIsaTy21UxHhDv1X76uOLX78/Tul6Kn7cxY2Lixg3x24XMW4ixxNx4yIuYnzgai2KHNdx4yI2YjyOFLfZ26buI8VTtShiXEWV+4hx1a66tlb23Xl9r26XqUjxXnNteZzmmo4bj8Z1fXX8uLjlchc/3lWR492OPdnrd+v9csbFj8927L6n48f7ue1r4sfHvCEOk1UAk2r1hRwAAAAAALBxsjDmYv421Lb5rhdfrQIAAAAAAGgJLuQAAAAAAAC0BF+tAgAAAAAAy4gf3/b4RA4AAAAAAEBLcCEHAAAAAADsKO973/vkoosukpmZGbnsssvks5/9bGPfP/3TP5Usy6J///f//l/T72Mf+5h893d/t/T7ffnu7/5u+fjHP76u9TbhQg4AAAAAANgxPvrRj8qNN94ot9xyi9x3333y/Oc/X1760pfK4cOHk8t9+ctfliNHjqz8u+SSS1Zqf/7nfy6vfOUr5brrrpO//du/leuuu05+4id+Qv7yL/9y3ev1shBC675NdvLkSdm3b5/8v7/fL3v2LF+LqlR94B7SfMhWbp+quqb2aDVr2+Xcyu3j5W5Te2RU106M7HIno3Z/5fZjw76pnXbthVE9p0FZmNrSsP4Zo6GrjUb2Olw5qutVmZlapfsO3fU71zcb1u18mKiN7DBRX1WP+65+e7kdGvsWvu/I9w2qZvsWg3ovyRLLLbd138rVysZapmoiIjJS7dLWMlOz40jl2mrZ4PsG396gl3Smns/M7jNZodod91NbHbufBt0uisa+oWPXEbXVOvVtERHJ67mG3O6HobBtUc2QNdeW66uvI6p52TR911hzoseSMkVXs47NuvS/xvlslqm25Q6UbbfThk2aTlaN77Oqdcxn4m07rtsUc0jGtSbmEy1XTdh3zNzMNgiJmn9+3FyzKjGOO46avqUfp/l8QIb2RCPTbVcLQ3UCs7RkawN7clMtqnrlziu2G/d+mXXqc9qsa88Psp46B3fnDlnfnhuLWja4cUSNU/VsLfTteUal2mU3d7W6XfbdOXUva2yXPTudsl/Xqqhm2/rPkLJv9zW/bNWr61Xf7Xu9up337T7S6dp2v1/vXzNdu1/O9Qb17e7A1PZ2F017j2qf010wtSd0T63c3t85YWrf0X3EtL9L1S/u2r+1tpOTpyp53FO/KidOnJC9e/du9XTOmG//nf09P3erFL2ZrZ7OVMrBovzd//e2iZ+zK664Qr7v+75P3v/+96/c94xnPEN+7Md+TG677bao/5/+6Z/Kv/yX/1IeeeQROeecc1Yd85WvfKWcPHlS/vAP/3Dlvn/1r/6VPO5xj5Pf/d3fXdN6m/CJHAAAAAAA0HonT540/5bcxXMRkcFgIPfee69cffXV5v6rr75aPve5zyXHf85zniPnn3++vPjFL5Y/+ZM/MbU///M/j8Z8yUtesjLmetbrcSEHAAAAAAC03oUXXij79u1b+bfap1weeughKctS9u/fb+7fv3+/HD16dNVxzz//fLnjjjvkYx/7mPze7/2ePO1pT5MXv/jF8pnPfGalz9GjR5NjrmW9TYgfBwAAAAAAy1ocP/7AAw+Yr1b1/Vc3lcz/HEII0X3f9rSnPU2e9rSnrbSvvPJKeeCBB+S//Jf/Ii94wQumGnOa9TbhEzkAAAAAAKD19u7da/6tdiHnCU94ghRFEX0K5tixY9GnZVK+//u/X77yla+stA8cOJAcc6PWK8KFHAAAAAAAsEP0ej257LLL5NChQ+b+Q4cOyVVXXTXxOPfdd5+cf/75K+0rr7wyGvPTn/70ypgbtV4RvloFAAAAAAB2kJtuukmuu+46ufzyy+XKK6+UO+64Qw4fPizXX3+9iIi89a1vlQcffFA++MEPiojIu9/9bvmu7/oueeYznymDwUA+/OEPy8c+9jH52Mc+tjLmG9/4RnnBC14gv/ZrvyYvf/nL5ROf+IT87//9v+Wee+6ZeL2TavWFnJNVkOpbkZGlyq8dBhs9eDrUD3O+clHgUbvO/ltyUeV6XL+OocvlrRI5vUVeNbYLl1PcKdaaf+pinlVOZ5XMQrYJnr5vrr4sWUWZwS7CW9V9gqlJF41qLjo9+QVNnxfdPJ9MjeufnXgr56vcivuO+0hbakvr2U3zjchoq0fJpGqGqSjbRFTstxaub+a2bwjN68h8zHqp3mY6dksHHetauX22tO2sU68ndNx8dFS53799W8eR+40ZtXVWuc+vVbd9NHm07zWMudy5carRrp/YUbIpvsgc1pj3veY4Zr9+/8LZjO9gryNBfNvFa58ttmCzbtQ+e0YixadZz9gI79SyU0SKa2OOGZNGjEfbY40R4+Piz/U4mYsUl8rFj+v6yNfUMS0VNy4iomLETdy4iISlOto5DGzMs28nj91bLRE3LmIjx03cuIhIt5eouRhxXXc1HTmeihsXsZHjlY8Y1/Hjibjx5XZzreqt3k/Exo0v10Njrer6OHK1L3btfpn36v2y6Njzrl7P7pd9Vd/VtfvlbKdu7+rY/XDOtXcXdfLPrtzWZrJ6nb3MzqdIvCEM3UlsN/N/v2DLtPg3cib1yle+Uo4fPy7vfOc75ciRI3LppZfKJz/5SXnyk58sIiJHjhyRw4cPr/QfDAbypje9SR588EGZnZ2VZz7zmfI//+f/lGuuuWalz1VXXSV33XWXvP3tb5f/+B//o1x88cXy0Y9+VK644oqJ1zupLITtfLRY3bfz7f/mi+fJ7j3Lb8L2Qo59o05dyDlZzZj2o+WuldsnyjlTO1HO1rdHs6Z2cmTHOT2q1zM/su/Ui6VtL6j6wP3xOhjVcx+V9nENXd/RSB2QRu7AVtbbpxq6N8lR1tjOhnaduTo2ZMOssSYikqt6PpTGvlHNtbNRaKwV0bKq7yi4vmHVfn4d0ThDd1KnTvLyYdlYExHJdH3krriok8MsqrmzV33S6S6UBN83cZHFjjnFS99dqDAncoW74JK7v9I7+kKO7Rt029fcuKIu1pgLN64d/EUVP3d1ISf4iyrRNaDmiz4hdSEndREh8UNmY66xpi9OTHHhInrck1rHxRGz/jPxpd4Nmis2EBdyNm49XMjhQs5W4UJOfCGnv3q/5bnatrmQE/V1F3L6al/suQs5fXUhp2vPC/t9u1/OdOv2XM/ua7u6dXt310Y07+sumvbezoK6bWvndk6v3H5i56SpHeicMO0LVf1JHfv31Ha6kHPyVCWPe+pX5cSJE+aHc8923/47+3v+7a1S9GbGL7CNlINF+bvfftuOec74jRwAAAAAAICW4EIOAAAAAABAS7T6N3IAAAAAAMDGycKYr9tuQ22b73rxiRwAAAAAAICW4EIOAAAAAABAS7T6q1UPV31ZqpavRem474GL3l5U+X6Lwf5K/qnK/lq6TrWadz8nr8fx8eM+bjwV8Z27z311VPx4FL1dRNnSa1TP10RHi0hIXM8LLjKi0rHcLlUhHUe+9ggZG2Nu1xlvnbWtJx1Hnje2fKBG6sropFHk4/qOG9cmOfpEqzV+5jBKK2nOjw+5S7EyCSQuDcSkcbm5dnxylxq3cltaJbhlPtGqcIlSlWonEq2WB2u4LTJ5NLlfj99eJhgr/cwnn70pXm7TRJXbVawn01vd3KwYc1PcmHXgzNmwhCltg/aDdUXST7PohH3Hfnx8E5KpouXWmEwV1aZJptI1P1efWqXaPlEyOhbpukuRNMlULqXKJ1PJUp36EwYutUolU4WRS7va7tRxPXMJUlnHtXW9a8+jTVKVS60KidSqKJlKp1b1XBJV152P62Qqn1ql0qdSKVW+HidRqfUl4sZ9PZlSJSKi4sgzl1pVqHOkXs/uszqlSkRkVkWOz3TsfqmTqvZ0bGrVnGvvVu1dha3tyut2N/OR4nY+NmmY+PFtawfEj7cdn8gBAAAAAABoCS7kAAAAAAAAtAQXcgAAAAAAAFqi1b+RAwAAAAAANk4Wwvp+H24LtG2+68UncgAAAAAAAFqCCzkAAAAAAAAt0eqvVj1U7pb5b8UOlyqHtnLXp1Lx44uVjyOfaawtVfXmGlR2040SObg+bty3C525mkjd88tt1NPnI7xNHHmUIFrP1W/n3HW2ceTrCdhuXi5L5qgmoqSdkNliYR6nl69ya/W+eZZYqaplLgbbzyfTUY5jUkvNVo/y2dUMo8kntmVI5AL7KPDKxbPq10aZiB8vEjURCaqdlS7ivBNUPzefPHd963YymlzExob751JPIRVNLmIic0Nqt3RTj/qaiPHUDp0eZ5qocrP+deQ6riu6vMGmxFVvomRc+iZo2/YxNunT0Wv+2PUmRIiLTBAjbsbd+EjxscumYsOn6av3RVdba8R4VCvduGW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      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from GPUSimulators import KP07_dimsplit\n",
    "importlib.reload(KP07_dimsplit)\n",
    "\n",
    "with Common.Timer(\"construct\") as t:\n",
    "    sim = KP07_dimsplit.KP07_dimsplit(**arguments)\n",
    "\n",
    "with Common.Timer(\"step\") as t:\n",
    "    t = sim.simulate(t_end)\n",
    "    \n",
    "with Common.Timer(\"download\") as t:\n",
    "    h1, hu1, hv1 = sim.download()\n",
    "\n",
    "plot(sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from GPUSimulators import WAF\n",
    "importlib.reload(WAF)\n",
    "\n",
    "with Common.Timer(\"construct\") as t:\n",
    "    sim = WAF.WAF(**arguments)\n",
    "\n",
    "with Common.Timer(\"step\") as t:\n",
    "    t = sim.simulate(t_end)\n",
    "    \n",
    "with Common.Timer(\"download\") as t:\n",
    "    h1, hu1, hv1 = sim.download()\n",
    "\n",
    "plot(sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ShallowWaterGPU",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}