i'd be curious to see this benchmark extended, as in my own experience toying with python-like interpreters, you get ~2x slowdown (like their end result) from just compiling to wasm/wasi with clang and running in any 'fast' runtime (e.g. node or wasmtime).
Any tip would be helpful so we can debug it. If your claims are accurate, we can easily get py2wasm even running faster than native CPython!
Note: we benchmarked in a M3 Max laptop, so maybe there's some difference there?
cat hello.py
print("Hello, Wasm!")
time python3 ./hello.py
Hello, Wasm!
________________________________________________________
Executed in 26.86 millis fish external
usr time 16.37 millis 0.13 millis 16.24 millis
sys time 7.25 millis 1.14 millis 6.11 millis
time wasmer hello.wasm
Hello, Wasm!
________________________________________________________
Executed in 84.77 millis fish external
usr time 50.26 millis 0.14 millis 50.12 millis
sys time 28.97 millis 1.21 millis 27.76 millis
time wasmtime hello.wasm
Hello, Wasm!
________________________________________________________
Executed in 141.72 millis fish external
usr time 120.86 millis 0.13 millis 120.72 millis
sys time 16.65 millis 1.20 millis 15.45 millisbut here's one test i've run just now:
using your test file, i've run this one-liner on my x86_64 linux laptop in https://pyodide.org/en/stable/console.html in chromium (v8) and natively (pyodide's urllib doesn't handle https for some reason):
import requests;exec(requests.get('https://gist.githubusercontent.com/syrusakbary/b318c97aaa8de6e8040fdd5d3995cb7c/raw/1c6cc96cf98bd7bd41c81ba9d10dc4d19b1c3e53/pystone.py').text)
pyodide: ~200k
i.e. ~2.4x slowdown
That should help us a lot to investigate further the difference in timing. I remember trying the py2wasm strategy about 4 years ago, and I got it running faster than native CPython, so there must be something we are missing!