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@gmail.com> wrote:
Hi,
On Thu, 17 Dec 2020 at 23:48, William ML Leslie william.leslie.ttg@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of additional code paths; invalidate some of the old code paths if you figure out that its optimizations are no longer valid (but invalidate only, not write a new version yet); and when you later see the old code path being generated again, optimize it differently.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.