exectimeit

Accurate Measurement of Small Execution Times

Measuring small execution times (especially for fast routines $< 100$ milliseconds) in Python can be extremely challenging. The traditional way of timing code is to take the difference of system times before and after a function execution. However, this is highly susceptible to two types of errors:

1. Random error: Environmental noise (OS scheduling, CPU frequency scaling) causes variation. This can be reduced by averaging multiple measurements.
2. Systematic error: The overhead of invoking the timing instructions themselves is added to the measured duration, which is difficult to filter out.

This library implements the mathematical model proposed by Carlos Moreno and Sebastian Fischmeister [1] to circumvent systematic measurement overhead.

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The Mathematics Behind It

Instead of measuring a single execution of a function $f$, we measure the total duration of executing the function sequentially $k$ times inside a single timing block, for $k = 1, 2, \dots, n$ (where $n$ is the number of repetitions, recommended $\ge 3$).

The total measured time $T_k$ for $k$ executions is modeled as a linear function: $$T_k = k \cdot t_{\text{exec}} + t_{\text{overhead}} + \epsilon_k$$

Where:

• $t_{\text{exec}}$ is the actual, true execution time of a single run.
• $t_{\text{overhead}}$ is the fixed overhead of starting/stopping the timer.
• $\epsilon_k$ is the random measurement error for trial $k$.

By fitting a simple linear regression $y = m \cdot x + b$ to the data points $(k, T_k)$:

• The slope ($m$) represents the true single-run execution time ($t_{\text{exec}}$), entirely free of the timer's start/stop overhead.
• The intercept ($b$) represents the fixed timer overhead ($t_{\text{overhead}}$).
• The Residual Standard Error (RSE) provides a precise measure of the variation or noise: $$rse = \sqrt{\frac{\sum (y_i - \hat{y}_i)^2}{n - 2}}$$

To eliminate loop iteration overhead in Python, exectimeit dynamically compiles and caches unrolled execution routines for each $k$.

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Installation

pip install exectimeit

Or install from GitHub:

pip install git+https://github.com/mariolpantunes/exectimeit.git@main

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Usage

Option 1: Direct function call wrapper

You can measure any callable using the timeit function. It returns an ExecTimeResult named tuple containing:

• time: estimated execution time per run (in seconds)
• rse: residual standard error of the linear fit (in seconds)
• value: the return value of the function

from exectimeit import timeit
import time

def my_fast_function(x, y):
    time.sleep(0.001)  # Simulating some work
    return x + y

# Measure with n=5 repetitions
result = timeit(5, my_fast_function, 2, y=3)

# You can access properties by name:
print(f"Time: {result.time:.6f}s, RSE: {result.rse:.6f}s, Return value: {result.value}")

# Or unpack like a standard tuple:
t, rse, val = result

Option 2: Decorator

You can decorate your functions with @exectime to automatically wrap them. When called, the decorated function will return the ExecTimeResult.

from exectimeit import exectime

@exectime(n=5)
def my_decorated_function(a):
    return a * a

# Executing the function returns the measurement named tuple:
t, rse, val = my_decorated_function(10)
print(f"Time: {t:.6f}s, Return value: {val}")

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Running Unit Tests

The test suite validates both deterministic mock-timing regression math and real-time execution. Run them with:

python3 -m unittest discover -s test

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Documentation

Detailed package documentation is hosted on GitHub Pages

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References

[1] C. Moreno and S. Fischmeister, "Accurate Measurement of Small Execution Times—Getting Around Measurement Errors," in IEEE Embedded Systems Letters, vol. 9, no. 1, pp. 17-20, March 2017, doi: 10.1109/LES.2017.2654160.

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Authors

• Mário Antunes - mariolpantunes

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License

This project is licensed under the MIT License - see the LICENSE file for details.