cout_CPU_projetTOSCA2020-2022.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mes notes sur l'estimation du cout des simulations proposées pour le projet TOSCA/OSTST à soumettre début octobre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Memo config OCCIPUT ORCA025:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nb de points config ORCA05 sans agrif\n",
    "N_occi=1440*1021*75\n",
    "\n",
    "# cout CPU config ORCA025 occiput:\n",
    "c_occi=5100 # heures CPU / an simulé\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Config mère ORCA05:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nb de points config ORCA05 sans agrif\n",
    "N0=720*510*75\n",
    "\n",
    "# cout CPU config ORCA05 sans agrif:\n",
    "c0=650  #(car 8 fois moins qu'ORCA025 OCCIPUT)\n",
    "# (c0 = c_occi/(f*f*f)  # heures CPU / an simulé)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "352"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nb de point du zoom (choisi identique à un NATL025L75)\n",
    "NZ = 486 * 530 *75\n",
    "\n",
    "# cout\n",
    "cz = 352 \n",
    "cz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### COUT TOTAL CONFIG ARGIF (mère+zoom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1002"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cout total de la config ORCA05 avec AGRIF: (heures CPU / an simulé)\n",
    "CT = c0 + cz\n",
    "CT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --> soit environ 1000 heures CPU / an simulé pour la config avec AGRIF\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plan d'experience proposé:\n",
    "\n",
    "    - 20 ans (spinup)\n",
    "    - 40 ans de climato avec rappel en sel\n",
    "    - 3 ensembles de 10 membres x40 ans\n",
    "\n",
    " **--> 1260 ans**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Cout total config AGRIF pour 1220 ans:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1260000"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1000*1260"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--> 1.5 millions d'heures CPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1512000.0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1260000+1260000*0.20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1260000"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((10*40 * 3) + 20 + 40) * 1000 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "---\n",
    "## Espace disque:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference occiput pour 1 mb, 1 an, par fréquence d'output:\n",
    "- 1m (variables 3-d): 15 Go\n",
    "- 5d (variables 2-d):  6.0 Go    \n",
    "- 1d (variables 2-d): 35 Go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70560"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(10*40*3 + 20 + 40) * (15+6+35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18900"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(10*40*3 + 20 + 40) * (15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --> Entre 19 et 70 To"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72300"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "6*75*4*40+15*20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "567000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(10*(40 * 3) + 20 + 40)*(6*75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "70*1.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}