|
| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "code", |
| 5 | + "execution_count": 1, |
| 6 | + "id": "b49ae6d6", |
| 7 | + "metadata": {}, |
| 8 | + "outputs": [], |
| 9 | + "source": [ |
| 10 | + "import pandas as pd\n", |
| 11 | + "import numpy as np\n", |
| 12 | + "import matplotlib.pyplot as plt\n", |
| 13 | + "import seaborn as sns\n", |
| 14 | + "\n", |
| 15 | + "plt.rcParams['figure.figsize'] = (16, 10)\n", |
| 16 | + "plt.rcParams['font.size'] = 11" |
| 17 | + ] |
| 18 | + }, |
| 19 | + { |
| 20 | + "cell_type": "code", |
| 21 | + "execution_count": 2, |
| 22 | + "id": "d236980d", |
| 23 | + "metadata": {}, |
| 24 | + "outputs": [ |
| 25 | + { |
| 26 | + "ename": "FileNotFoundError", |
| 27 | + "evalue": "[Errno 2] No such file or directory: 'BASELINE_bench.csv'", |
| 28 | + "output_type": "error", |
| 29 | + "traceback": [ |
| 30 | + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", |
| 31 | + "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", |
| 32 | + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 17\u001b[39m\n\u001b[32m 14\u001b[39m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m 15\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m pd.DataFrame(data)\n\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m baseline = \u001b[43mparse_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mBASELINE_bench.csv\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 18\u001b[39m custom = parse_csv(\u001b[33m'\u001b[39m\u001b[33mCUSTOM_SIMD_bench.csv\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 20\u001b[39m merged = baseline.merge(custom, on=\u001b[33m'\u001b[39m\u001b[33mBenchmark\u001b[39m\u001b[33m'\u001b[39m, suffixes=(\u001b[33m'\u001b[39m\u001b[33m_baseline\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33m_custom\u001b[39m\u001b[33m'\u001b[39m))\n", |
| 33 | + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 2\u001b[39m, in \u001b[36mparse_csv\u001b[39m\u001b[34m(filepath)\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mparse_csv\u001b[39m(filepath):\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mr\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[32m 3\u001b[39m lines = f.readlines()[\u001b[32m1\u001b[39m:]\n\u001b[32m 5\u001b[39m data = []\n", |
| 34 | + "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/secp256k1/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py:343\u001b[39m, in \u001b[36m_modified_open\u001b[39m\u001b[34m(file, *args, **kwargs)\u001b[39m\n\u001b[32m 336\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[32m0\u001b[39m, \u001b[32m1\u001b[39m, \u001b[32m2\u001b[39m}:\n\u001b[32m 337\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 338\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mIPython won\u001b[39m\u001b[33m'\u001b[39m\u001b[33mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m by default \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 339\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 340\u001b[39m \u001b[33m\"\u001b[39m\u001b[33myou can use builtins\u001b[39m\u001b[33m'\u001b[39m\u001b[33m open.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 341\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m343\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", |
| 35 | + "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'BASELINE_bench.csv'" |
| 36 | + ] |
| 37 | + } |
| 38 | + ], |
| 39 | + "source": [ |
| 40 | + "def parse_csv(filepath):\n", |
| 41 | + " with open(filepath, 'r') as f:\n", |
| 42 | + " lines = f.readlines()[1:]\n", |
| 43 | + " \n", |
| 44 | + " data = []\n", |
| 45 | + " for line in lines:\n", |
| 46 | + " line = line.strip()\n", |
| 47 | + " if line and ',' in line and not line.endswith(','):\n", |
| 48 | + " parts = line.split(',')\n", |
| 49 | + " if len(parts) >= 3:\n", |
| 50 | + " try:\n", |
| 51 | + " data.append({'Benchmark': parts[0].strip(), 'Time': float(parts[2])})\n", |
| 52 | + " except:\n", |
| 53 | + " continue\n", |
| 54 | + " return pd.DataFrame(data)\n", |
| 55 | + "\n", |
| 56 | + "baseline = parse_csv('BASELINE_bench.csv')\n", |
| 57 | + "custom = parse_csv('CUSTOM_SIMD_bench.csv')\n", |
| 58 | + "\n", |
| 59 | + "merged = baseline.merge(custom, on='Benchmark', suffixes=('_baseline', '_custom'))\n", |
| 60 | + "merged['improvement'] = ((merged['Time_baseline'] - merged['Time_custom']) / merged['Time_baseline']) * 100" |
| 61 | + ] |
| 62 | + }, |
| 63 | + { |
| 64 | + "cell_type": "code", |
| 65 | + "execution_count": null, |
| 66 | + "id": "8442b12d", |
| 67 | + "metadata": {}, |
| 68 | + "outputs": [], |
| 69 | + "source": [ |
| 70 | + "sorted_data = merged.sort_values('improvement', ascending=False)\n", |
| 71 | + "top10 = sorted_data.head(10)\n", |
| 72 | + "bottom10 = sorted_data.tail(10)\n", |
| 73 | + "filtered = pd.concat([top10, bottom10])" |
| 74 | + ] |
| 75 | + }, |
| 76 | + { |
| 77 | + "cell_type": "code", |
| 78 | + "execution_count": null, |
| 79 | + "id": "aa07550a", |
| 80 | + "metadata": {}, |
| 81 | + "outputs": [], |
| 82 | + "source": [ |
| 83 | + "heatmap_data = filtered.set_index('Benchmark')[['improvement']]\n", |
| 84 | + "\n", |
| 85 | + "plt.figure(figsize=(8, 12))\n", |
| 86 | + "sns.heatmap(heatmap_data, annot=True, fmt='.1f', cmap='RdYlGn', center=0, \n", |
| 87 | + " cbar_kws={'label': 'Performance Improvement (%)'})\n", |
| 88 | + "plt.title('CUSTOM_SIMD vs BASELINE Performance (Top/Bottom 10)', fontsize=14, fontweight='bold')\n", |
| 89 | + "plt.ylabel('')\n", |
| 90 | + "plt.tight_layout()\n", |
| 91 | + "plt.show()" |
| 92 | + ] |
| 93 | + } |
| 94 | + ], |
| 95 | + "metadata": { |
| 96 | + "kernelspec": { |
| 97 | + "display_name": ".venv", |
| 98 | + "language": "python", |
| 99 | + "name": "python3" |
| 100 | + }, |
| 101 | + "language_info": { |
| 102 | + "codemirror_mode": { |
| 103 | + "name": "ipython", |
| 104 | + "version": 3 |
| 105 | + }, |
| 106 | + "file_extension": ".py", |
| 107 | + "mimetype": "text/x-python", |
| 108 | + "name": "python", |
| 109 | + "nbconvert_exporter": "python", |
| 110 | + "pygments_lexer": "ipython3", |
| 111 | + "version": "3.12.3" |
| 112 | + } |
| 113 | + }, |
| 114 | + "nbformat": 4, |
| 115 | + "nbformat_minor": 5 |
| 116 | +} |
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