[pypy-dev] Contributing Polyhedral Optimisations in PyPy

[muke101]

Hi, so to update you both I have decided to pursue this project after all, I'm very excited to work on PyPy.

To reiterate my objective, I'll be trying to formulate a way to augment the JIT optimiser to expose enough information such that more advanced optimisations can be implemented, with Polyhedral compatibility in mind. Of the ideas suggested, I'm currently leaning towards trying to create a second optimisation layer for sufficiently hot code, which can take into account more of the program at once, as this seems similar to what other JIT compilers already employ. This is open to the problem of assumptions being invalidated that Armin bought up (if I understood correctly), but similar implementations like in the paper I referred to below have formulated methods to accommodate for this. I think the key for PyPy would be figuring out how to track the correct metadata from previously seen traces such that the bytecode from relevant control paths can be bought together to work on, and mainly reconstructing entire loops once individual sufficiently hot traces are found. Once this is done then actually any number of optimisations could be preformed. The JIT compiler in the JavaScriptCore engine compiles the hottest bytecode down to LLVM-IR and sends it through the LLVM back end. I had looked into similar possibilities for Python, and it seems only a subset of the language can be compiled to LLVM-IR through Numa though, which is a shame. If focusing on just Polyhedral optimisations though a possibility could be to write a SCoP detector for Python bytecode specifically, raise it to Polyhedral representation and then import it into LLVM's Polly tool, but this is getting ahead a bit.

I'll be getting to grips with PyPy's codebase soon, after I'm comfortable with the fundamentals of tracing JIT's. Do you have any suggestions on where to begin specifically for what I'm looking to do? I imagine generally all this will be within the JIT optimiser, but if there's anything specific you can think of please let me know.

Thanks.


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‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐
On Friday, 18 December 2020 18:15, muke101 <muke101 at protonmail.com> wrote:

> Thanks both of you for getting back to me, these definitely seem like problems worth thinking about first. Looking into it, there has actually been some research already on implementing Polyhedral optimisations in a JIT optimiser, specifically in JavaScript. It's paper (http://impact.gforge.inria.fr/impact2018/papers/polyhedral-javascript.pdf) seems to point out the same problems you both bring up, like SCoP detection and aliasing, and how it worked around them.
>
> For now then I'll try and consider how ambitious replicating these solutions would be and if they would map into PyPy from JS cleanly - please let me know if any other hurdles come to mind in the meantime though.
>
> Thanks again for the advise.
>
> ‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐
> On Friday, 18 December 2020 18:03, Armin Rigo armin.rigo at gmail.com wrote:
>
> > Hi,
> > On Thu, 17 Dec 2020 at 23:48, William ML Leslie
> > william.leslie.ttg at gmail.com wrote:
> >
> > > The challenge with implementing this in the pypy JIT at this point is
> > > that the JIT only sees one control flow path. That is, one loop, and
> > > the branches taken within that loop. It does not find out about the
> > > outer loop usually until later, and may not ever find out about the
> > > content of other control flow paths if they aren't taken.
> >
> > Note that strictly speaking, the problem is not that you haven't seen
> > yet other code paths. It's Python, so you never know what may happen
> > in the future---maybe another code path will be taken, or maybe
> > someone will do crazy things with `sys._getframe()` or with the
> > debugger `pdb`. So merely seeing all paths in a function doesn't
> > really buy you a lot. No, the problem is that emitting machine code
> > is incremental at the granularity of code paths. At the point where
> > we see a new code path, all previously-seen code paths have already
> > been completely optimized and turned into machine code, and we don't
> > keep much information about them.
> > To go beyond this simple model, what we have so far is that we can
> > "invalidate" previous code paths at any point, when we figure out that
> > they were compiled using assumptions that no longer hold. So using
> > it, it would be possible in theory to do any amount of global
> > optimizations: save enough additional information as you see each code
> > path; use it later in the optimization of additional code paths;
> > invalidate some of the old code paths if you figure out that its
> > optimizations are no longer valid (but invalidate only, not write a
> > new version yet); and when you later see the old code path being
> > generated again, optimize it differently. It's all doable, but
> > theoretical so far: I don't know of any JIT compiler that seriously
> > does things like that. It's certainly worth a research paper IMHO.
> > It also looks like quite some work. It's certainly not just "take
> > some ideas from [ahead-of-time or full-method] compiler X and apply
> > them to PyPy".
> > A bientôt,
> > Armin.