PyBench is a powerful Python benchmarking tool inspired by Geekbench, designed to analyze and optimize the performance of modern CPUs when running Python applications. As the landscape of Python evolves, ensuring that new versions deliver speed improvements is crucial. PyBench facilitates this by serving as a synthetic CPU benchmark tool, applicable across a range of devices from laptops to enterprise servers.

Key Features:

• Evaluate Python performance on various hardware configurations.
• Optimize CPU performance specifically for Python workloads.
• Provide insights into the performance improvements of different Python versions.

Example Usage:

Simply run the following command to initiate the benchmarking process:

> python3 pybench.py

During the benchmark, you'll see results like:

Compress using BZ2 algorithm:
[========================================] 100.0% 0:00:15
Compress using LZMA algorithm:
[========================================] 100.0% 0:00:16
Calculate Pi using Wallis product:
[========================================] 100.0% 0:00:13
Calculate Fibonacci numbers recursively:
[========================================] 100.0% 0:00:17
Calculate Fibonacci numbers iteratively:
[========================================] 100.0% 0:00:15
Multiply matrices:
[========================================] 100.0% 0:00:16
Benchmark time: 93.9806 seconds

Benchmark Results:

• Python 3.12 on Apple M1 (power): 59.4037s
• Python 3.12 on Apple M1 (battery): 93.9806s
• Python 3.11 on Qualcomm Snapdragon 765G: 187.0722s
• Python 3.11 on Intel Core (Skylake, IBRS, 3792 MHz): 205.7209s
• Python 3.13 on Intel Xeon (2.20 GHz): 329.1787s

The objective is clear: the lower the time, the better the performance. For example, the Intel Core processor running at nearly 4 GHz is the powerhouse behind the hosting of Subreply, a small yet dynamic social network.

With PyBench, take the next step in optimizing your Python applications and ensure you’re leveraging the best performance across your systems!