Improve performance of pyo programs — Pyo 1.0.4 documentation

Pyo 1.0.4 documentation

# Improve performance of pyo programs

This document lists various tips that help to improve the performance of your pyo programs.

## Python tips

There is not much you can do at the Python level because once the script has finished its execution run, almost all computations are done in the C level of pyo. Nevertheless, there is these two tricks to consider:

### Adjust the interpreter’s “check interval”

You can change how often the interpreter checks for periodic things with sys.setcheckinterval(interval). The defaults is 100, which means the check is performed every 100 Python virtual instructions. Setting it to a larger value may increase performance for programs using threads.

### Use the subprocess or multiprocessing modules

You can use the subprocess or multiprocessing modules to spawn your processes on multiple processors. From the python docs:

> The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows.

Here is a little example of using the multiprocessing module to spawn a lot of sine wave computations to multiple processors.

    #!/usr/bin/env python
    # encoding: utf-8
    """
    Spawning lot of sine waves to multiple processes.
    From the command line, run the script with -i flag.

    Call quit() to stop the workers and quit the program.

    """
    import time
    import multiprocessing
    from random import uniform
    from pyo import Server, SineLoop

    class Group(multiprocessing.Process):
        def __init__(self, num_of_sines):
            super(Group, self).__init__()
            self.daemon = True
            self._terminated = False
            self.num_of_sines = num_of_sines

        def run(self):
            # All code that should run on a separated
            # core must be created in the run() method.
            self.server = Server()
            self.server.deactivateMidi()
            self.server.boot().start()

            freqs = [uniform(400,800) for i in range(self.num_of_sines)]
            self.oscs = SineLoop(freq=freqs, feedback=0.1, mul=.005).out()

            # Keeps the process alive...
            while not self._terminated:
                time.sleep(0.001)

            self.server.stop()

        def stop(self):
            self._terminated = True

    if __name__ == '__main__':
        # Starts four processes playing 500 oscillators each.
        jobs = [Group(500) for i in range(4)]
        [job.start() for job in jobs]

        def quit():
            "Stops the workers and quit the program."
            [job.stop() for job in jobs]
            exit()

### Avoid memory allocation after initialization

Dynamic memory allocation (malloc/calloc/realloc) tends to be nondeterministic; the time taken to allocate memory may not be predictable, making it inappropriate for real time systems. To be sure that the audio callback will run smoothly all the time, it is better to create all audio objects at the program’s initialization and call their stop(), play(), out() methods when needed.

Be aware that a simple arithmetic operation involving an audio object will create a Dummy object (to hold the modified signal), thus will allocate memory for its audio stream AND add a processing task on the CPU. Run this simple example and watch the process’s CPU growing:

    from pyo import *
    import random

    s = Server().boot()

    env = Fader(0.005, 0.09, 0.1, mul=0.2)
    jit = Randi(min=1.0, max=1.02, freq=3)
    sig = RCOsc(freq=[100,100], mul=env).out()

    def change():
        freq = midiToHz(random.randrange(60, 72, 2))
        # Because `jit` is a PyoObject, both `freq+jit` and `freq-jit` will
        # create a `Dummy` object, for which a reference will be created and
        # saved in the `sig` object. The result is both memory and CPU
        # increase until something bad happens!
        sig.freq = [freq+jit, freq-jit]
        env.play()

    pat = Pattern(change, time=0.125).play()

    s.gui(locals())

An efficient version of this program should look like this:

    from pyo import *
    import random

    s = Server().boot()

    env = Fader(0.005, 0.09, 0.1, mul=0.2)
    jit = Randi(min=1.0, max=1.02, freq=3)
    # Create a `Sig` object to hold the frequency value.
    frq = Sig(100)
    # Create the `Dummy` objects only once at initialization.
    sig = RCOsc(freq=[frq+jit, frq-jit], mul=env).out()

    def change():
        freq = midiToHz(random.randrange(60, 72, 2))
        # Only change the `value` attribute of the Sig object.
        frq.value = freq
        env.play()

    pat = Pattern(change, time=0.125).play()

    s.gui(locals())

### Don’t do anything that can trigger the garbage collector

The garbage collector of python is another nondeterministic process. You should avoid doing anything that can trigger it. So, instead of deleting an audio object, which can turn out to delete many stream objects, you should just call its stop() method to remove it from the server’s processing loop.

## Pyo tips

Here is a list of tips specific to pyo that you should consider when trying to reduce the CPU consumption of your audio program.

### Mix down before applying effects

It is very easy to over-saturate the CPU with pyo, especially if you use the multi-channel expansion feature. If your final output uses less channels than the number of audio streams in an object, don’t forget to mix it down (call its mix() method) before applying effects on the sum of the signals.

Consider the following snippet, which create a chorus of 50 oscillators and apply a phasing effect on the resulting sound:

    src = SineLoop(freq=[random.uniform(190,210) for i in range(50)],
                   feedback=0.1, mul=0.01)
    lfo = Sine(.25).range(200, 400)
    phs = Phaser(src, freq=lfo, q=20, feedback=0.95).out()

This version uses around 47% of the CPU on my Thinkpad T430, i5 3320M @ 2.6GHz. The problem is that the 50 oscillators given in input of the Phaser object creates 50 identical Phaser objects, one for each oscillator. That is a big waste of CPU. The next version mixes the oscillators into a stereo stream before applying the effect and the CPU consumption drops to ~7% !

    src = SineLoop(freq=[random.uniform(190,210) for i in range(50)],
                   feedback=0.1, mul=0.01)
    lfo = Sine(.25).range(200, 400)
    phs = Phaser(src.mix(2), freq=lfo, q=20, feedback=0.95).out()

When costly effects are involved, this can have a very drastic impact on the CPU usage.

### Stop your unused audio objects

Whenever you don’t use an audio object (but you want to keep it for future uses), call its stop() method. This will inform the server to remove it from the computation loop. Setting the volume to 0 does not save CPU (everything is computed then multiplied by 0), the stop() method does. My own synth classes often looks like something like this:

    class Glitchy:
        def __init__(self):
            self.feed = Lorenz(0.002, 0.8, True, 0.49, 0.5)
            self.amp = Sine(0.2).range(0.01, 0.3)
            self.src = SineLoop(1, self.feed, mul=self.amp)
            self.filt = ButLP(self.src, 10000)

        def play(self, chnl=0):
            self.feed.play()
            self.amp.play()
            self.src.play()
            self.filt.out(chnl)
            return self

        def stop(self):
            self.feed.stop()
            self.amp.stop()
            self.src.stop()
            self.filt.stop()
            return self

### Control attribute with numbers instead of PyoObjects

Objects internal processing functions are optimized when plain numbers are given to their attributes. Unless you really need audio control over some parameters, don’t waste CPU cycles and give fixed numbers to every attribute that don’t need to change over time. See this comparison:

    n = Noise(.2)

    # ~5% CPU
    p1 = Phaser(n, freq=[100,105], spread=1.2, q=10,
                feedback=0.9, num=48).out()

    # ~14% CPU
    p2 = Phaser(n, freq=[100,105], spread=Sig(1.2), q=10,
                feedback=0.9, num=48).out()

Making the spread attribute of p2 an audio signal causes the frequency of the 48 notches to be recalculated every sample, which can be a very costly process.

### Check for denormal numbers

From wikipedia:

> In computer science, denormal numbers or denormalized numbers (now often called subnormal numbers) fill the underflow gap around zero in floating-point arithmetic. Any non-zero number with magnitude smaller than the smallest normal number is ‘subnormal’.

The problem is that some processors compute denormal numbers very slowly, which makes grow the CPU consumption very quickly. The solution is to wrap the objects that are subject to denormals (any object with an internal recursive delay line, ie. filters, delays, reverbs, harmonizers, etc.) in a Denorm object. Denorm adds a little amount of noise, with a magnitude just above the smallest normal number, to its input. Of course, you can use the same noise for multiple denormalizations:

    n = Noise(1e-24) # low-level noise for denormals

    src = SfPlayer(SNDS_PATH+"/transparent.aif")
    dly = Delay(src+n, delay=.1, feedback=0.8, mul=0.2).out()
    rev = WGVerb(src+n).out()

### Use a PyoObject when available

Always look first if a PyoObject does what you want, it will always be more efficient than the same process written from scratch.

This construct, although pedagogically valid, will never be more efficient, in term of CPU and memory usage, than a native PyoObject (Phaser) written in C.

    a = BrownNoise(.02).mix(2).out()

    lfo = Sine(.25).range(.75, 1.25)
    filters = []
    for i in range(24):
        freq = rescale(i, xmin=0, xmax=24, ymin=100, ymax=10000)
        filter = Allpass2(a, freq=lfo*freq, bw=freq/2, mul=0.2).out()
        filters.append(filter)

It is also more efficient to use Biquadx(stages=4) than a cascade of four Biquad objects with identical arguments.

### Avoid trigonometric computation

Avoid trigonometric functions computed at audio rate (Sin, Cos, Tan, Atan2, etc.), use simple approximations instead. For example, you can replace a clean Sin/Cos panning function with a cheaper one based on Sqrt:

    # Heavier
    pan = Linseg([(0,0), (2, 1)]).play()
    left = Cos(pan * math.pi * 0.5, mul=0.5)
    right = Sin(pan * math.pi * 0.5, mul=0.5)
    a = Noise([left, right]).out()

    # Cheaper
    pan2 = Linseg([(0,0), (2, 1)]).play()
    left2 = Sqrt(1 - pan2, mul=0.5)
    right2 = Sqrt(pan2, mul=0.5)
    a2 = Noise([left2, right2]).out()

### Use approximations if absolute precision is not needed

When absolute precision is not really important, you can save precious CPU cycles by using approximations instead of the real function. FastSine is an approximation of the sin function that can be almost twice cheaper than a lookup table (Sine). I plan to add more approximations like this one in the future.

### Re-use your generators

Some times it possible to use the same signal for parallel purposes. Let’s study the next process:

    # single white noise
    noise = Noise()

    # denormal signal
    denorm = noise * 1e-24
    # little jitter around 1 used to modulate frequency
    jitter = noise * 0.0007 + 1.0
    # excitation signal of the waveguide
    source = noise * 0.7

    env = Fader(fadein=0.001, fadeout=0.01, dur=0.015).play()
    src = ButLP(source, freq=1000, mul=env)
    wg = Waveguide(src+denorm, freq=100*jitter, dur=30).out()

Here the same white noise is used for three purposes at the same time. First, it is used to generate a denormal signal. Then, it is used to generate a little jitter applied to the frequency of the waveguide (that adds a little buzz to the string sound) and finally, we use it as the excitation of the waveguide. This is surely cheaper than generating three different white noises without noticeable difference in the sound.

### Leave ‘mul’ and ‘add’ attributes to their defaults when possible

There is an internal condition that bypass the object “post-processing” function when mul=1 and add=0. It is a good practice to apply amplitude control in one place instead of messing with the mul attribute of each objects.

    # wrong
    n = Noise(mul=0.7)
    bp1 = ButBP(n, freq=500, q=10, mul=0.5)
    bp2 = ButBP(n, freq=1500, q=10, mul=0.5)
    bp3 = ButBP(n, freq=2500, q=10, mul=0.5)
    rev = Freeverb(bp1+bp2+bp3, size=0.9, bal=0.3, mul=0.7).out()

    # good
    n = Noise(mul=0.25)
    bp1 = ButBP(n, freq=500, q=10)
    bp2 = ButBP(n, freq=1500, q=10)
    bp3 = ButBP(n, freq=2500, q=10)
    rev = Freeverb(bp1+bp2+bp3, size=0.9, bal=0.3).out()

### Avoid graphical updates

Even if they run in different threads, with different priorities, the audio callback and the graphical interface of a python program are parts of a unique process, sharing the same CPU. Don’t use the Server’s GUI if you don’t need to see the meters or use the volume slider. Instead, you could start the script from command line with -i flag to leave the interpreter alive.

    $ python -i myscript.py

### List of CPU intensive objects

Here is a non-exhaustive list of the most CPU intensive objects of the library.

- Analysis

    - Yin

    - Centroid

    - Spectrum

    - Scope

- Arithmetic

    - Sin

    - Cos

    - Tan

    - Tanh

    - Atan2

- Dynamic

    - Compress

    - Gate

- Special Effects

    - Convolve

- Prefix Expression Evaluator

    - Expr

- Filters

    - Phaser

    - Vocoder

    - IRWinSinc

    - IRAverage

    - IRPulse

    - IRFM

- Fast Fourier Transform

    - CvlVerb

- Phase Vocoder

    - Almost every objects!

- Signal Generators

    - LFO

- Matrix Processing

    - MatrixMorph

- Table Processing

    - Granulator

    - Granule

    - Particule

    - OscBank

- Utilities

    - Resample