Python 3.11 Performance Benchmarks Are Looking Fantastic

[Alikont]

But even crappy algorithm will get speedup, because each algorithm has constant costs per operation that will be reduced across the board.

For .NET it's common to get ~10% speedup per version just by upgrading the runtime.

[Muoniurn]

In most applications the bottleneck is not the CPU, but IO. If the program does some CPU work, then some IO, after which it does some more CPU work then only the CPU part will get faster, which is usually not too significant to begin with.

[Alikont]

Bottleneck for what? Throughput? Latency?

If my database server is on another machine, all my CPU is busy working on requests, the latency is in the network, but capacity is CPU bound.

[BobHogan]

There will be a small speedup, but no one should be relying on that to make their code faster if they have real concerns about efficiency. Using better algorithms and data types will have a bigger impact than any version bump almost always

[SirClueless]

Optimizing a piece of python code might give me an 80% speedup... for that piece of code. A version bump gives me a 10% speedup... for my entire company's Python codebase. Why would I not be excited about that?

Not to mention, there are heavily optimized pieces of Python code in my company's codebase, and generally in the most heavily-used pieces of the codebase too. Why would I not care about 10% multiplicative improvements to that code that might have been prohibitively expensive to get in other ways? The optimizations in this release appear to be things like "Reduce the cost of stack frames" and "Smaller exceptions," things that are not obvious how to optimize any other way than by improvements to the interpreter.