176 lines
5.6 KiB
Plaintext
176 lines
5.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "da7c16b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from scipy.optimize import curve_fit\n",
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"from matplotlib.cm import get_cmap"
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]
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},
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{
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"cell_type": "markdown",
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"id": "47341b1d",
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"metadata": {},
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"source": [
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"# Alltoall "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1cc39aab",
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"metadata": {},
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"outputs": [
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31mnotebook controller is DISPOSED. \n",
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"\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
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]
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}
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],
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"source": [
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"df_multinode = pd.read_csv(\"../data/data-multi-defand100cflag.csv\",delimiter = \",\")\n",
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"df_multinode['benchmark_type'].unique()\n",
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"df_gather = df_multinode[df_multinode[\"benchmark_type\"]==\"Bcast\"][df_multinode['msg_size_bytes']>1024][df_multinode['off_cache_flag']==-1]\n",
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"df_gather.columns.tolist()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4336d3c6",
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"metadata": {},
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"outputs": [
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31mnotebook controller is DISPOSED. \n",
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"\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
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]
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}
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],
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"source": [
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"def model(proc_num, alpha, beta, msg_size):\n",
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" return (alpha * msg_size * (proc_num - 72) * 72) / (12.5 * 1e3) + 1e6*beta\n",
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"\n",
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"results = []\n",
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"msg_sizes = sorted(df_gather['msg_size_bytes'].unique())\n",
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"n_rows = int(np.ceil(len(msg_sizes) / 3))\n",
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"n_cols = min(len(msg_sizes), 3)\n",
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"fig, axes = plt.subplots(n_rows, n_cols, figsize=(5*n_cols, 4*n_rows), squeeze=False)\n",
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"cmap = get_cmap('tab10')\n",
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"\n",
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"for idx, (msg_size, group) in enumerate(df_gather.groupby('msg_size_bytes')):\n",
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" x = group['proc_num'].values.copy()\n",
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" y = group['t_avg_usec'].values.copy()\n",
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" sorted_indices = np.argsort(x)\n",
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" x = x[sorted_indices]\n",
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" y = y[sorted_indices]\n",
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" fit_func = lambda proc_num, alpha, beta: model(proc_num, alpha, beta, msg_size)\n",
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" popt, _ = curve_fit(fit_func, x, y, bounds=([1, 0], [np.inf, np.inf]))\n",
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" alpha, beta = popt\n",
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" results.append({'msg_size_bytes': msg_size, 'alpha': alpha, 'beta': beta})\n",
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"\n",
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" x_fit = np.linspace(min(x), max(x), 100)\n",
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" y_fit = fit_func(x_fit, alpha, beta)\n",
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" y_speed = model(x_fit,1,0,msg_size)\n",
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" row, col = divmod(idx, n_cols)\n",
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" ax = axes[row][col]\n",
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"\n",
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" color = cmap(idx % 10)\n",
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" # ax.scatter(x, y/1e6, label='Data', color=color)\n",
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" ax.plot(x, y/1e6, label='Data', color=color)\n",
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" # ax.plot(x_fit, y_fit/1e6, linestyle='--', color=color, alpha=0.5, label='Fit')\n",
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" # ax.plot(x_fit, y_speed/1e6, linestyle='--', color='red', alpha=0.1, label='Fit')\n",
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" ax.set_title(f'msg_size: {msg_size} bytes')\n",
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" ax.set_xlabel('num. proc.')\n",
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" ax.set_ylabel('Average Time [s]')\n",
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" ax.set_xticks(x)\n",
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" ax.grid(True)\n",
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" max_data =(x[-1]-72)*72*msg_size\n",
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" min_data =(x[0]-72)*72*msg_size\n",
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"\n",
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" textstr = \"\"\n",
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" # if(max_data > 1e9):\n",
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" # textstr+=f\"max data = {max_data/1e9:0.2f}GB\\n\" \n",
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" # else:\n",
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" # textstr+=f\"max data = {max_data/1e6:0.2f}MB\\n\" \n",
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"\n",
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" # if(min_data > 1e9):\n",
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" # textstr+=f\"min data = {min_data/1e9:0.2f}GB\\n\" \n",
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" # else:\n",
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" # textstr+=f\"min data = {min_data/1e6:0.2f}MB\\n\" \n",
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" # textstr += r\"$\\alpha$\" +f\"= {alpha:.3e}\\n\"+r\"$b_{eff}=$\"+f\"{12.5/alpha:0.3f}Gbps\\n\"+\\\n",
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" # r\"$\\beta$\"+f\"= {beta:.3e} s\"\n",
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" # ax.text(0.95, 0.05, textstr, transform=ax.transAxes,\n",
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" # fontsize=10, verticalalignment='bottom',\n",
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" # horizontalalignment='right',\n",
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" # bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))\n",
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"\n",
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"fig.suptitle('Alltoall Time Fit per Message Size\\nDots = Data Points | Dashed Lines = Fits\\n off_mem=-1', fontsize=14)\n",
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"fig.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
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"# plt.savefig(\"plots/alltoall_analysis.png\",dpi=300)\n",
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"plt.show()\n",
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"\n",
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"fit_results = pd.DataFrame(results)\n",
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"fit_results['inv_alpha'] = 1 / fit_results['alpha']\n",
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"print(fit_results)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ce632d6f",
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"metadata": {},
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"outputs": [
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31mnotebook controller is DISPOSED. \n",
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"\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
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]
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}
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],
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"source": [
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"df_gather[df_gather['msg_size_bytes']==1048576]"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "data",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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