การวิเคราะห์ประสิทธิภาพของพื้นฐาน Python

Before you port anything, measure the Python app as it is today. This baseline is the number every later speedup is compared against. 📊

Most of a program's time hides in a tiny slice of code. Profiling shows you exactly where, so you port the slow part and skip the rest.

First, time the full task end to end. This wall-clock total is your headline number and the easiest thing to track over time.

Python's cProfile breaks the run down per function, so you can see which calls eat the most time across the whole program.

Sort the report by total time and look at the top rows. The hot path is usually one or two functions doing the real heavy lifting.

Inside the hot function, hunt for a loop over many elements doing math. That inner loop is almost always the part worth porting to Mojo.

A loop is only worth porting if it runs a lot. Note the data size, since a million iterations matters far more than ten.

Write down the baseline time and the hot function's share. Later you will compare against these numbers to prove the port paid off.

If most time is spent reading files or the network, that is I/O, not compute. Mojo speeds up math, so be sure the bottleneck is CPU work.

A good port target is compute-bound: lots of arithmetic over many elements with little waiting. That is exactly where Mojo shines.

Choose the single slowest compute hot path to rewrite first. One clear target keeps your capstone focused and easy to measure.

Pick the best first move when accelerating a Python app.

Measure the Python baseline, profile to find the compute-bound hot loop, and pick one clear target. Now you know exactly what to port. 🎯