{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# This file is part of nannos\n# License: GPLv3\n%matplotlib notebook"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Benchmark Modal\n\nNumerical backends performace comparison, with GPU acceleration, as run on Modal (https://modal.com)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import os\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nbackends = [\"numpy\", \"scipy\", \"autograd\", \"jax\", \"torch\"]\ninputs = [(b, \"cpu\") for b in backends]\ninputs += [(b, \"gpu\") for b in [\"jax\", \"torch\"]]\n\n\nt = []\n\nplt.figure()\n\nfor i in inputs:\n    backend, device = i\n    fname = f\"modal/benchmark_{backend}_{device}.npz\"\n    arch = np.load(fname)\n    times = arch[\"times\"]\n    times_all = arch[\"times_all\"]\n    real_nh = arch[\"real_nh\"]\n    nh = arch[\"nh\"]\n\n    t.append(times_all)\n\n    plt.plot(real_nh, times_all, \"-o\", label=f\"{backend} {device}\")\nplt.legend()\nplt.xlabel(\"number of harmonics\")\nplt.ylabel(\"time [s]\")\nplt.xscale(\"log\")\nplt.yscale(\"log\")\n\n\nplt.figure()\nfor j, i in enumerate(inputs):\n    backend, device = i\n    plt.plot(real_nh, t[0] / t[j], \"-o\", label=f\"{backend} {device}\")\nplt.legend()\nplt.xlabel(\"number of harmonics\")\nplt.ylabel(\"speedup vs numpy\")\nplt.xscale(\"log\")\n# plt.yscale(\"log\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import nannos.utils.jupyter\n%nannos_version_table"
      ]
    }
  ],
  "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": "3.12.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}