SciPy: Using fft.set_workers() function (4 examples)

Introduction to fft.set_workers()

The fft.set_workers() function is a context manager in the SciPy FFT submodule that allows for parallel execution of FFT computations. This is particularly useful when working with large datasets or requiring real-time processing speeds. The number of workers specified corresponds to the number of threads used for parallel computations. If not specified, the maximum number of available processors on the machine is used.

Before diving into the examples, ensure you have SciPy installed in your environment. If not, you can install it using pip:

pip install scipy

Example 1: Basic Usage of fft.set_workers()

In our first example, we’ll start with a simple FFT operation using a mock dataset. Here, we demonstrate how to apply the set_workers() function to execute the FFT.

import numpy as np
from scipy.fft import fft, set_workers
# Create a simple signal
signal = np.random.rand(1024)
# Perform FFT with the default number of workers
with set_workers(1):
    fft_result = fft(signal)
print(fft_result)

Output (vary):

[ 5.08708189e+02-0.j         -7.69543271e+00-5.86478064j
  2.06440509e-01+4.77008932j ... -7.33420901e+00+0.36434256j
  2.06440509e-01-4.77008932j -7.69543271e+00+5.86478064j]

This example used one worker to perform the FFT, which is useful for understanding the function but may not offer a significant performance advantage.

Example 2: Increasing Workers for Performance Gain

Next, we increase the number of workers to potentially improve performance. This is particularly relevant for larger datasets.

import numpy as np
from scipy.fft import fft, set_workers
from scipy.fft import next_fast_len

signal = np.random.rand(5000000)
optimized_length = next_fast_len(len(signal))
with set_workers(4):
    fft_result = fft(signal, n=optimized_length)
print(len(fft_result))

Output:

5000000

The next_fast_len function finds the nearest optimal size for FFT processing, improving efficiency. Here, we utilized four workers, demonstrating a simple way to potentially enhance computing speed.

Example 3: Real-World Data Processing

In the third example, we apply fft.set_workers() to a real-world dataset, focusing on signal processing for audio data.

import numpy as np
from scipy.fft import fft, set_workers
import scipy.io.wavfile as wavfile

# Load an audio file
rate, data = wavfile.read('example_audio.wav')
# Assuming stereo audio. Taking just one channel
mono_data = data[:,0]
with set_workers(8):
    fft_result = fft(mono_data)
    frequencies = np.fft.fftfreq(len(mono_data), 1/rate)
print(frequencies[np.abs(fft_result) > 1000])

This example demonstrates using set_workers() for processing an audio signal, taking advantage of multicore systems to speed up the analysis for real-time or near-real-time applications.

Example 4: Advanced Applications and Benchmarks

For our final example, we compare the performance of fft.set_workers() with varying numbers of workers to understand its impact on computation time.

import numpy as np
from scipy.fft import fft, set_workers
import time

np.random.seed(2024)
signal = np.random.rand(5000000)

for workers in range(1, 9):
    start_time = time.time()
    with set_workers(workers):
        fft(signal)
    print(f'Workers: {workers}, Time: {time.time() - start_time}')

Output:

Workers: 1, Time: 0.07139205932617188
Workers: 2, Time: 0.05623221397399902
Workers: 3, Time: 0.05038785934448242
Workers: 4, Time: 0.04983186721801758
Workers: 5, Time: 0.05101895332336426
Workers: 6, Time: 0.0498659610748291
Workers: 7, Time: 0.049367666244506836
Workers: 8, Time: 0.051492929458618164

This experiment highlights how increasing the number of workers can significantly reduce computation time, especially on systems with multiple cores and large datasets.

Conclusion

Throughout this tutorial, we’ve explored how to use the fft.set_workers() function in the scipy.fft module for parallel FFT computations. Starting with basic applications and moving towards more complex scenarios, we have shown that adjusting the number of workers can notably enhance performance, especially on multicore systems dealing with large datasets. Incorporating this knowledge in your data processing workflows can lead to more efficient and timely analyses.