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@krischer
Last active August 29, 2015 14:06
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{
"worksheets": [
{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "# FFT Tests\n\nGoal: Find the fastest way to perform an FFT + IFFT on 1000 real valued arrays of equal length. The length of each arrays can vary from about 5000 to 150000.\n\n\nIn general using one big array and the `axis` argument does not appear to be worth the hassle. No matter the dimension/memory order is was always slower then a dump loop over each array. The Python call overhead is probably not big enough to offset some implementation complications. Or the `axis` argument is also implemented in pure Python?"
},
{
"metadata": {},
"cell_type": "code",
"input": "%pylab inline\n\nimport multiprocessing\nimport pyfftw\nimport scipy.fftpack as sci_fft\nimport time\n\n\nCOUNT = 1000\nNPTS = 100000\n\nDATA = np.random.rand(COUNT, NPTS)",
"prompt_number": 1,
"outputs": [
{
"output_type": "stream",
"text": "Populating the interactive namespace from numpy and matplotlib\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## NumPy\n\nThe order of dimensions and row/column major ordering used has been tested to be the fasted one of all combinations"
},
{
"metadata": {},
"cell_type": "code",
"input": "dat = np.require(DATA.copy(), dtype=\"float32\", requirements=[\"C\", \"A\"])\na = time.time()\nfor d in dat:\n _temp = np.fft.rfft(d)\n np.fft.irfft(_temp)\nb = time.time()\nprint \"NumPy loop float32 per FFT + IFFT: %f\" % ((b - a) / COUNT)\n\n\ndat = np.require(DATA.copy(), dtype=\"float64\", requirements=[\"C\", \"A\"])\na = time.time()\nfor d in dat:\n _temp = np.fft.rfft(d)\n np.fft.irfft(_temp)\nb = time.time()\nprint \"NumPy loop float64 per FFT + IFFT: %f\" % ((b - a) / COUNT)",
"prompt_number": 2,
"outputs": [
{
"output_type": "stream",
"text": "NumPy loop float32 per FFT + IFFT: 0.003218\nNumPy loop float64 per FFT + IFFT: 0.003310\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## SciPy\n\nAgain the column/row major ordering used it the fasted one of all combinations."
},
{
"metadata": {},
"cell_type": "code",
"input": "dat = np.require(DATA.copy(), dtype=\"float32\", requirements=[\"C\", \"A\"])\na = time.time()\nfor d in dat:\n _temp = sci_fft.rfft(d)\n sci_fft.irfft(_temp)\nb = time.time()\nprint \"Dumb scipy loop float32 per FFT + IFFT: %f\" % ((b - a) / COUNT)\n\n\ndat = np.require(DATA.copy(), dtype=\"float64\", requirements=[\"C\", \"A\"])\na = time.time()\nfor d in dat:\n _temp = sci_fft.rfft(d)\n sci_fft.irfft(_temp)\nb = time.time()\nprint \"Dumb scipy loop float64 per FFT + IFFT: %f\" % ((b - a) / COUNT)",
"prompt_number": 3,
"outputs": [
{
"output_type": "stream",
"text": "Dumb scipy loop float32 per FFT + IFFT: 0.001882\nDumb scipy loop float64 per FFT + IFFT: 0.001990\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## FFTW\n\nUses the FFTW plans directly to have to fiddle with the cache settings and more control.\n\nThe `FFTW_ESTIMATE` flag is the fastest way to create a plan and appears to have little impact on the time of the FFT. The plan creation with `FFTW_ESTIMATE` is 3 orders of magnitudes faster then with `FFTW_MEASURE`."
},
{
"metadata": {},
"cell_type": "code",
"input": "dat = pyfftw.n_byte_align_empty(DATA.shape, 4, dtype='float32')\ndat[:] = DATA\noutput = pyfftw.n_byte_align_empty(DATA.shape[1] // 2 + 1, 8, dtype='complex64')\n\na = time.time()\n# Create plans. Not included in time measurements as a fixed cost that can be done at initialization time.\nFFT = pyfftw.FFTW(dat[0].copy(), output, direction='FFTW_FORWARD', flags=('FFTW_ESTIMATE', ), threads=1)\nIFFT = pyfftw.FFTW(output, dat[0].copy(), direction='FFTW_BACKWARD', flags=('FFTW_ESTIMATE', ), threads=1)\nb = time.time()\nprint \"Time to create plan:\", b - a\n\n# Sanity check against numpys output...\ntemp = dat[0].copy()\nFFT(input_array=temp.copy())\nnp.testing.assert_allclose(output, np.fft.rfft(temp), rtol=1E-3, atol=1E-10)\nIFFT(output_array=temp)\nnp.testing.assert_allclose(temp, np.fft.irfft(output), rtol=1E-2, atol=1E-10)\n\na = time.time()\nfor d in dat:\n FFT(input_array=d)\n IFFT(input_array=output, output_array=d)\nb = time.time()\n\nprint \"FFTW loop float32 per FFT + IFFT: %f\" % ((b - a) / COUNT)\n\n\n\ndat = pyfftw.n_byte_align_empty(DATA.shape, 8, dtype='float64')\ndat[:] = DATA\noutput = pyfftw.n_byte_align_empty(DATA.shape[1] // 2 + 1, 16, dtype='complex128')\n\na = time.time()\n\n# Create plans. Not included in time measurements as a fixed cost that can be done at initialization time.\nFFT = pyfftw.FFTW(dat[0].copy(), output, direction='FFTW_FORWARD', flags=('FFTW_ESTIMATE', ), threads=1)\nIFFT = pyfftw.FFTW(output, dat[0].copy(), direction='FFTW_BACKWARD', flags=('FFTW_ESTIMATE', ), threads=1)\nb = time.time()\nprint \"Time to create plan:\", b - a\n\na = time.time()\n\nfor d in dat:\n FFT(input_array=d)\n IFFT(input_array=output, output_array=d)\n \nb = time.time()\nprint \"FFTW loop float64 per FFT + IFFT: %f\" % ((b - a) / COUNT)",
"prompt_number": 4,
"outputs": [
{
"output_type": "stream",
"text": "Time to create plan: 0.0197148323059\nFFTW loop float32 per FFT + IFFT: 0.000897\nTime to create plan: 0.0228831768036\nFFTW loop float64 per FFT + IFFT: 0.001437\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Plot\n\nHere we only compare SciPy and FFTW. NumPy's FFT has a well known issue for some array lengths as it does not contain optimizations for certain array lenghts. We will furthermore use single precision as it is a bit faster and likely accurate enough.\n\nThe FFTW times do not include the plan building times as they are a constant cost that can occur at initialization time. Therefore the comparision is a bit unfair but use case oriented."
},
{
"metadata": {},
"cell_type": "code",
"input": "scipy_times = []\nfftw_times = []\nsample_counts = []\n\nfor npts in range(5000, 150000, 1000):\n print npts,\n sample_counts.append(npts)\n \n # The aligned array can also be used for the scipy version.\n dat = pyfftw.n_byte_align_empty((COUNT, npts), 4, dtype='float32')\n dat[:] = np.random.randn(COUNT, npts)\n output = pyfftw.n_byte_align_empty(npts // 2 + 1, 8, dtype='complex64')\n \n a = time.time()\n for d in dat:\n _temp = sci_fft.rfft(d)\n sci_fft.irfft(_temp)\n b = time.time()\n scipy_times.append((b - a) / COUNT)\n \n\n # Create plans.\n FFT = pyfftw.FFTW(dat[0].copy(), output, direction='FFTW_FORWARD', flags=('FFTW_ESTIMATE', ), threads=1)\n IFFT = pyfftw.FFTW(output, dat[0].copy(), direction='FFTW_BACKWARD', flags=('FFTW_ESTIMATE', ), threads=1)\n \n a = time.time()\n for d in dat:\n FFT(input_array=d)\n IFFT(input_array=output, output_array=d)\n b = time.time()\n fftw_times.append((b - a) / COUNT)",
"prompt_number": 5,
"outputs": [
{
"output_type": "stream",
"text": "5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000 21000 22000 23000 24000 25000 26000 27000 28000 29000 30000 31000 32000 33000 34000 35000 36000 37000 38000 39000 40000 41000 42000 43000 44000 45000 46000 47000 48000 49000 50000 51000 52000 53000 54000 55000 56000 57000 58000 59000 60000 61000 62000 63000 64000 65000 66000 67000 68000 69000 70000 71000 72000 73000 74000 75000 76000 77000 78000 79000 80000 81000 82000 83000 84000 85000 86000 87000 88000 89000 90000 91000 92000 93000 94000 95000 96000 97000 98000 99000 100000 101000 102000 103000 104000 105000 106000 107000 108000 109000 110000 111000 112000 113000 114000 115000 116000 117000 118000 119000 120000 121000 122000 123000 124000 125000 126000 127000 128000 129000 130000 131000 132000 133000 134000 135000 136000 137000 138000 139000 140000 141000 142000 143000 144000 145000 146000 147000 148000 149000\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "plt.figure(figsize=(15, 10))\n\nplt.plot(sample_counts, scipy_times, color=\"red\", label=\"scipy\")\nplt.plot(sample_counts, fftw_times, color=\"blue\", label=\"fftw\")\nplt.xlabel(\"Sample Count\")\nplt.ylabel(\"Time per transform pair\")\nplt.legend()\nplt.show()",
"prompt_number": 10,
"outputs": [
{
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P/ZqDXRAppdMRcT3wTmALeH1K6baIeNXo669LKb09Il4YEbcDjwKv7Hrs6Kmv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\nGcDtNSxko9YZ1QrKelR6rZtDGdzzJ7DcwM2quTlty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"text": "<matplotlib.figure.Figure at 0x13500d0d0>",
"output_type": "display_data",
"metadata": {}
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
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