simd_speedup.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "```csv\n",
    "1 , 0.021891078900000003\n",
    "2 , 0.0152584929\n",
    "4 , 0.0062463407000000002\n",
    "8 , 0.0032286318000000003\n",
    "16 , 0.0031803633999999996\n",
    "32 , 0.00281839\n",
    "64 , 0.0029921934000000003\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = np.loadtxt('float32_no_greeks.txt', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [int(x) for x in results[:, 0]]\n",
    "Y = results[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(range(len(X)), Y, width=0.6, align='center', edgecolor='black')\n",
    "\n",
    "plt.xlabel('SIMD vector width')\n",
    "plt.ylabel('Time (s)')\n",
    "plt.title('Time to compute 200,000 Monte Carlo paths using SIMD (AVX2, float32)')\n",
    "plt.xticks(range(len(X)), X)\n",
    "plt.savefig('simd_speedup_raw.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AAD Results\n",
    "\n",
    "```\n",
    "1 , 0.090354849200000004\n",
    "2 , 0.045406369999999995\n",
    "4 , 0.025888369300000009\n",
    "8 , 0.014865453900000001\n",
    "16 , 0.013454404499999999\n",
    "32 , 0.011659963800000003\n",
    "64 , 0.011684249800000001\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "aad_results = np.loadtxt('float32_w_greeks.txt', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "aadX = [int(x) for x in aad_results[:, 0]]\n",
    "aadY = aad_results[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(range(len(aadX)), aadY, width=0.6, align='center', edgecolor='black', color='green')\n",
    "\n",
    "plt.xlabel('SIMD vector width')\n",
    "plt.ylabel('Time (s)')\n",
    "plt.title('Time to compute 200,000 Monte Carlo paths and Greeks using SIMD (AVX2, float32)')\n",
    "plt.xticks(range(len(aadX)), aadX)\n",
    "plt.savefig('simd_speedup_aad.png')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}