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Add logo, add intro to chapter one, add note about hardware requirements
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288
sources/02c Multithreading.ipynb
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288
sources/02c Multithreading.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Multi-Threading Comparison\n",
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"\n",
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"This notebook contains a performance comparison of different methods to process the NDVI calculations.\n",
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"\n",
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"The `%%timeit` cell magic runs the cell content multiple times and outputs statistics on those multiple runs, thereby reducing factors such as garbage collection pauses etc."
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from multiprocessing import Pool, cpu_count\n",
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"from numpy import ma\n",
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"from pathlib import Path\n",
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"import rasterio as r"
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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": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Number of files: 27\n"
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]
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}
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],
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"source": [
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"test_files = list(Path('output/ndvi').glob('*.tif'))\n",
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"print(f'Number of files: {len(test_files)}')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The function we test with:"
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def average(file_path):\n",
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" with r.open(file_path) as src:\n",
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" data = src.read(1, masked=True)\n",
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" return file_path, ma.average(data)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## In a single process\n",
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"### Time to process a single file"
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"36.2 ms ± 42.6 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
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]
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}
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],
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"source": [
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"%%timeit\n",
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"average(test_files[0])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Time to process all files"
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"980 ms ± 7.38 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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]
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}
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],
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"source": [
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"%%timeit\n",
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"averages = [avg for avg in map(average, test_files)]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Increasing the list size"
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"4.86 s ± 10.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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]
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}
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],
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"source": [
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"%%timeit\n",
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"averages = [avg for avg in map(average, test_files * 5)]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Time when using a worker pool\n",
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"\n",
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"Number of CPUs the multiprocessing pools can access:"
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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": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"4"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"cpu_count()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### On One element"
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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": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"277 ms ± 3.92 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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]
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}
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],
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"source": [
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"%%timeit\n",
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"with Pool() as pool:\n",
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" averages = [avg for avg in pool.map(average, test_files[:1])]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### On the complete list"
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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": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"630 ms ± 8.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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]
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}
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],
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"source": [
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"%%timeit\n",
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"with Pool() as pool:\n",
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" averages = [avg for avg in pool.map(average, test_files)]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Increasing the list size"
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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": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2.1 s ± 20 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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]
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}
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],
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"source": [
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"%%timeit\n",
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"with Pool() as pool:\n",
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" averages = [avg for avg in pool.map(average, test_files * 5)]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Result\n",
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"\n",
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"As we can see when processing a single element, multiprocessing comes with an overhead.\n",
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"When the list to be processed is sufficiently large, we get a reduction in processing time of roughly 30%-50%, depending on list size.\n",
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"\n",
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"Averaging the masked array is a fairly simple operation that scales in $O(N)$ with the size of the input array.\n",
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"The time reduction should be even higher for more complex tasks."
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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",
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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": "3.8.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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