Comment by ghj | Hacker News Reader

Comment by ghj | Hacker News Reader

I stalked the author's linkedin and notice he has competitive programming experience: https://www.topcoder.com/members/kmod/details/?track=DATA_SC... (and top 15 putnam, ICPC world finals, etc)

I wonder if he would be interested in optimizing for purely algorithmic tasks?

There are a lot active and successful CPython and PyPy users on https://atcoder.jp/. For example:

https://atcoder.jp/contests/practice2/submissions?f.Task=&f.... (the user "maspy" is rated at 2750 using only cpython!!!)

https://atcoder.jp/contests/practice2/submissions?f.Task=&f.... (though pypy is more practical)

I am linking to atcoder because their testing data is public so you can rerun contestants solutions using both pyston/cpython/pypy for benchmarking purposes: https://www.dropbox.com/sh/arnpe0ef5wds8cv/AAAk_SECQ2Nc6SVGi...

Right now, other than a handful of people who figured out how to make numba's jit work, only pypy is viable for competitive programming. I wonder if you can do better than pypy?

There are also a few red coders on codeforces.com who mostly use pypy (cpython is completely unviable there because numpy and numba is not installed)

https://codeforces.com/submissions/pajenegod

https://codeforces.com/submissions/conqueror_of_tourist

But codeforces' test cases aren't public anyway so it's not as relevant.

All my CodeJam solutions are in Python :)

While we could certainly go in this direction, we're not planning to, because in our experience optimizations for different workloads are largely distinct, and this use case is already handled well by PyPy.

Isn't this use case the scientific computing use case? That's a fairly large part of the ecosystem to give up on!

I think it's still a relatively low effort way (just need to write a scraper) to create a benchmark on a diverse set of algorithmic tasks that have clearcut criteria on AC/TLE/WA. PyPy is often 10x faster than cpython on these problems (and just 2x slower than equivalent C++ solution) so it will be a much nicer headline too if you can achieve similar performances!

Though I can also see how it can be completely irrelevant for server workloads. Pypy's unicode is so slow, some people on codeforces still use pypy2 over pypy3 just to avoid it. And c extensions is so bad on pypy, you can often get better performance on cpython if you need to use numpy.

This is just a comment on my personal use of Python for competitive programming: I've never used numpy for competitive programming or thought that it would be a good tool for that. PyPy seems like a great solution for the highly-numerical algorithms that these contests tend to lead to.

So I would not call this "scientific computing". Personally I consider competitive programming to be it's own use case.

And as much as we want to improve scientific computing in Python, it's very hard since the work is done in C. Our current hope is to help mixed workloads, such as doing a decent amount of data-preprocessing in Python before handing off to C code.

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