99 lines
13 KiB
Plaintext
99 lines
13 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"\n",
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"\n",
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"fcuda = \"iter_cuda.txt\"\n",
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"fcpu = \"iter.txt\"\n",
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"\n",
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"v_cuda = []\n",
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"v_cpu = []\n",
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"t_cuda = []\n",
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"t_cpu = []\n",
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"\n",
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"\n",
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"# open file and read\n",
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"with open(fcuda, \"r\") as f:\n",
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" cuda = f.readlines()\n",
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" t = 0\n",
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" for line in cuda:\n",
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" v = float(line.split(',')[0].split(\":\")[1])\n",
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" v_cuda.append(v)\n",
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" t += float(line.split(',')[-1])\n",
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" t_cuda.append(t)\n",
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"\n",
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"with open(fcpu, \"r\") as f:\n",
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" cpu = f.readlines()\n",
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" t = 0\n",
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" for line in cpu:\n",
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" v = float(line.split(',')[0].split(\":\")[1])\n",
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" v_cpu.append(v)\n",
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" t += float(line.split(',')[-1])\n",
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" t_cpu.append(t)\n",
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"\n",
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"# plot\n",
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"plt.scatter(t_cuda, v_cuda, label=\"cuda\")\n",
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"plt.scatter(t_cpu, v_cpu, label=\"cpu\")\n",
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"#plt.plot(v_cuda, label=\"cuda\")\n",
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"#plt.plot(v_cpu, label=\"cpu\")\n",
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"\n",
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"plt.legend()\n",
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"plt.show()"
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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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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.9.1 64-bit ('3.9.1')",
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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": "3.9.1"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "02f83e1f4cd9619657a6845405e2dd67c4de23753ba48bca5dce2ebf57b3813a"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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