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PyPy 4.0.0 Released – A Jit with SIMD Vectorization and More

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Re: PyPy 4.0.0 Released – A Jit with SIMD Vectorization and More

#11
> We would also like to thank our contributors (7 new ones since PyPy 2.6.0)

That's very good news, since monetary contributions don't seem to be abunding (none of the goals have been met according to pypy.org). Maybe it's a marketing issue, like said before?

Thanks for all the effort made.

Re: PyPy 4.0.0 Released – A Jit with SIMD Vectorization and More

#12

> We would also like to thank our contributors (7 new ones since PyPy 2.6.0) and encourage new people to join the project. PyPy has many layers and we need help with all of them This doesn't match my experience of the PyPy project. I found a tiny bug in the stdlib matching against CPython, went into IRC to ask a question about test running to be sure I got it right and was quickly engaged in conversation about why I…

Is this a complaint or a compliment to the developers?

Re: PyPy 4.0.0 Released – A Jit with SIMD Vectorization and More

#13

Guys, what's a "trace optimizer"? Google is failing me by returning results about MySQL.

Search https://www.google.com?q=trace+optimizer+compilers

According to wikipedia: https://en.wikipedia.org/wiki/Tracing_just-in-time_compilati...

Tracing just-in-time compilation is a technique used by virtual machines to optimize the execution of a program at runtime. This is done by recording a linear sequence of frequently executed operations, compiling them to native machine code and executing them. This is opposed to traditional just-in-time (JIT) compilers that work on a per-method basis.

Re: PyPy 4.0.0 Released – A Jit with SIMD Vectorization and More

#16

Guys, what's a "trace optimizer"? Google is failing me by returning results about MySQL.

I assume it's a reference to tracing JIT (https://en.wikipedia.org/wiki/Tracing_just-in-time_compilati...).

It's a JIT that traces runtime execution, rather than looking at whole method or function bodies. In particular it eliminates control flow, which simplifies optimisation.

Re: PyPy 4.0.0 Released – A Jit with SIMD Vectorization and More

#17
post #8
post #5

Kudos to the pypy team! I suspect pypy would benefit from some kind of marketing campaign. It rarely lets me down and yet lots of python devs don't know about it. The most popular package(s) that don't work with it have been numpy, scipy, etc. When I first encountered pypy, there was a numpypy and I wouldn't be surprised if that's different now. But bottom line, tons of python code out there needs more performance an…

> But bottom line, tons of python code out there needs more performance and has no dependency on numpy or any C extensions. What they need is nice JIT engines like PyPy and not to switch languages. If no one invested in improving implementations for modern languages and switched to something lower level all the time, we would still be using Fortran for business applications.

I think you guys are saying the same thing. Ie, because they don't need to switch languages, and PyPy is a JIT engine for Python, these tons of Python code would benefit from if the devs were aware of PyPy.

Re: PyPy 4.0.0 Released – A Jit with SIMD Vectorization and More

#19
My experience with PyPy was that it was faster for mapreduce jobs without JSON parsing and slower for JSON-heavy jobs. Because, I assume, CPython benefits from a C implementation of simplejson and PyPy doesn't. Does anyone know if this has changed? (I'll probably install 4.0 and see for myself, but curious if anyone knows what I should expect.)

Re: PyPy 4.0.0 Released – A Jit with SIMD Vectorization and More

#20

> We would also like to thank our contributors (7 new ones since PyPy 2.6.0) and encourage new people to join the project. PyPy has many layers and we need help with all of them This doesn't match my experience of the PyPy project. I found a tiny bug in the stdlib matching against CPython, went into IRC to ask a question about test running to be sure I got it right and was quickly engaged in conversation about why I…

That's a shame, it can sometimes be tough to pick out a good manageable intro-bug on an open source project you want to contribute to. Perhaps it was a new-ish core dev who was also keen to cut his teeth on small/doable bugs?
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