Created
May 18, 2018 11:54
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Intel distribution for Python benchmark
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import sys | |
import time | |
import numpy as np | |
import numpy.random as rn | |
def fftbench(size): | |
mat = rn.rand(size, size) + 1j * rn.randn(size, size) | |
start = time.time() | |
# 2D transform on a complex-valued matrix: | |
result = np.fft.fft2(mat) | |
end = time.time() | |
print(size, end - start) | |
if __name__ == '__main__': | |
for size in sys.argv[1:]: | |
fftbench(int(size)) |
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# basic anaconda | |
python fftbench.py 1 10 100 1000 5000 7000 10000 15000 > basic-anaconda | |
# install intel-enhanced anaconda in a new environment | |
conda config --add channels intel | |
conda create --name intelpy intelpython3_full python=3 | |
conda activate intelpy | |
python fftbench.py 1 10 100 1000 5000 7000 10000 15000 > intel-anaconda |
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"import pandas as pd" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"anaconda = pd.read_csv('basic-anaconda', delim_whitespace=True, header=None)\n", | |
"intel = pd.read_csv('intel-anaconda', delim_whitespace=True, header=None)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import matplotlib.pyplot as plt\n", | |
"%matplotlib inline" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
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z3yTw3bbDTB3bm8Gdg+0OR9WBqhbm12NdGxgjIhlAEPBorUWllLLFwnXJvPH9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\nRIYGcnGvVkS0CyCqfSCtmvn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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f392c8a4ef0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"plt.plot(anaconda[0], anaconda[1], label='basic anaconda')\n", | |
"plt.plot(intel[0], intel[1], label='intel-enhanced anaconda')\n", | |
"plt.xlabel('Matrix size')\n", | |
"plt.ylabel('seconds')\n", | |
"plt.legend()\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3 (anaconda)", | |
"language": "python", | |
"name": "python3-anaconda" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.6.2" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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This implements the benchmark from https://www.infoworld.com/article/3187484/software/how-does-a-20x-speed-up-in-python-grab-you.html