{"id":14163,"date":"2026-04-27T12:15:33","date_gmt":"2026-04-27T11:15:33","guid":{"rendered":"https:\/\/www.blopig.com\/blog\/?p=14163"},"modified":"2026-04-27T12:18:50","modified_gmt":"2026-04-27T11:18:50","slug":"speeding-up-python-through-profiling","status":"publish","type":"post","link":"https:\/\/www.blopig.com\/blog\/2026\/04\/speeding-up-python-through-profiling\/","title":{"rendered":"Speeding up python through profiling"},"content":{"rendered":"\n

Python is a shockingly slow language. A test<\/a> on a raspberry pi of simply “turn this pin on and off as fast as you can” gave the results below.<\/p>\n\n\n\n

System<\/th>Library<\/th>Speed<\/th><\/tr><\/thead>
Shell<\/td>\/proc\/mem access<\/td>2.8 kHz<\/td><\/tr>
Shell \/ gpio utility<\/td>WiringPi gpio utility<\/td>40 Hz<\/td><\/tr>
Python<\/td>RPI.GPIO<\/td>70 kHz<\/td><\/tr>
Python<\/td>wiringPi2 bindings<\/td>28 kHz<\/td><\/tr>
Ruby<\/td>wiringPi bindings<\/td>21 kHz<\/td><\/tr>
C<\/td>Native library<\/td>22 MHz<\/td><\/tr>
C<\/td>BCM 2835<\/td>5.4 MHz<\/td><\/tr>
C<\/td>wiringPi<\/td>4.1 – 4.6 MHz<\/td><\/tr>
Perl<\/td>BCM 2835<\/td>48 kHz<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n\n\n\n\n

So, how do we speed up python? The simple answer is not to use python, and use a faster language. Whilst that may sound facetious, some of the most commonly used python libraries are actually written in, or have cores in much faster languages.<\/p>\n\n\n\n

Library<\/th>~ % Python<\/th>Notes<\/th><\/tr><\/thead>
PyTorch<\/td>62<\/td>Core in C++<\/td><\/tr>
Numpy<\/td>60<\/td>Core in C<\/td><\/tr>
TensorFlow<\/td>20<\/td>Core in C++<\/td><\/tr>
OpenCV<\/td>3<\/td>Core in C++<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

If using pre-made libraries with fast cores still doesn’t provide the speed-up you need, then what? Find the slow bit – Test it,\u00a0 Tidy it,\u00a0 Time it.<\/em>
Test it <\/strong>– Quite often, things are slow because they’re a bit buggy. So a bit more of a concerted effort testing the code wouldn’t go amiss.
Tidy it<\/strong> – If the code’s spaghetti-like and has lost its elegance as you’ve had to put in workarounds for workarounds, perhaps it’s going through seldom used pathways and incurring an efficiency hit every time. Refactoring your code can help its performance.
Time it <\/strong>– If the above are no help, time to reach for the big guns and time each section of code and see where the slow-down is happening. This can be done with a profiler and\/or a visualiser, a selection of which are mentioned below.<\/p>\n\n\n\n