[wdroz]

> py2wasm converts your Python programs to WebAssembly, running them at 3x faster speeds

This is clearly written in the article, but I hope that the impatient readers will understand that this is 3 times faster than the CPython wasm, not the native CPython.

[5-]

even more specifically, 3 times faster than wasmer's build of cpython (whatever that is), running on their runtime.

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).

[syrusakbary]

Hey, I'd love to reproduce the ~2x slowdown you commented from running the workload in Native CPython compared to Wasm CPython (in any runtime, browser or outside).

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?

[kelp]

Here is a trivial example, also on a M3 Max:

  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 millis

[5-]

note that i'm not claiming 2x slowdown for cpython.

but 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)

native: ~470k

pyodide: ~200k

i.e. ~2.4x slowdown

[syrusakbary]

Thanks!

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!

[IshKebab]

Yeah more honest would have been "2x slower"!

[williamstein]

Even more honest would be “on one particular microbenchmark that everybody optimizes for”.

That said, this seems like a really cool project that could have some real value! It still fully depends on having the full CPython runtime environment, but that means it could work correctly with most existing code and libraries (including numpy and script).

[wasiwin]

From what I could tell, Nuitka dynamically loads extension modules so this wouldn't work with Webassembly modules which are statically linked. If I could be enhanced to automatically build wheels of packages like numpy and statically link them, then that could bring extensions to Wasm and be very cool. IIRC, Google does this with their Python apps so the concept does exist but don't think I've seen any OSS tooling trying that.