When All is Said and Done

In the Beginning

Back when I was young and naïve at the beginning of the summer, I proposed to continue the work that a few PyPy developers and I had worked on, a reimplementation of NumPy in RPython. The project holds a lot of promise, as PyPy can generate a JIT compiler for itself and its components written in RPython. With a NumPy array written in RPython, the PyPy JIT can see inside of it and from that can make far more optimizations than it could otherwise. Since the PyPy JIT is especially good at optimizing CPU/computationally expensive code, bringing the two together could go a long way to bridge the gap between Python performance, and statically compiled languages.

As luck would have it, my project was categorized by the Python Software Foundation as a NumPy project, rather than a PyPy project, whose developers I’d been bugging and asking questions for some time. I soon came into contact with Stéfan van der Walt, a member of the NumPy Steering Committee. After consulting with him and the NumPy mailing list, it was decided that most people would not find a super fast NumPy array very useful by itself. For it to matter to most people, it would need to be able to do everything that the existing NumPy array does, and someone brought up the point that there is a great deal of C and Cython code already written which interacts with NumPy arrays, and it’s important that my project would allow these things.

So my project ballooned to a huge size, and I thought I could handle it all. The new burden of full compatibility was to be attacked by porting NumPy to PyPy and providing an easy interface for switching to and from NumPy and micronumpy arrays. Unfortunately, this pursuit wasn’t very fruitful, as PyPy’s CPyExt isn’t yet equipped to handle the demands of a module as all encompassing as NumPy. I spent a fair amount of time simply implementing symbols to satisfy the dependencies of NumPy. I made some significant changes to NumPy which are currently sitting in my git repository on github. I don’t know what the future holds for them unfortunately (If the NumPy refactor is completed soon enough, I may be able to sidestep CPyExt which will be faster anyways).

Midterms

Around midterms I had micronumpy arrays working reasonably well enough that they could run the convolve benchmark, and handily beat NumPy arrays (twice as fast is fairly impressive). However, the point is to demonstrate that the JIT can speed up code to near compiled code, theoretically removing the need to rewrite large portions of python code in C or Cython. By this time, it was becoming clear that getting NumPy to work with PyPy was not going to happen over the summer. I’ve adjusted my expectations, NumPy working on PyPy is still on my TODO list but won’t be completed this summer. This might be for the better anyways, as NumPy is being refactored to be less Python (and therefore CPython) centric, as a result, in the near future I may be able to completely avoid CPyExt and use RPython’s foreign function interface to call NumPy code directly.

The Final Stretch

One of the beautiful things about PyPy’s JIT is that it’s generated, not hard coded, so I didn’t have do to anything in order to have micronumpy be JITed. Unfortunately, in the past three days or so, I’ve discovered that my code no longer works with the JIT. I’ve done all I can to figure out what’s wrong, and I can’t fix it on my own. Diving into the JIT in the last 24 hours of the summer of code surely won’t bear any fruit. I’ve put up my distress signal on the mailing list, and hopefully this issue can be resolved in time to provide some awesome benchmark results. If not, at least I can get this resolved in the next couple of weeks and then move on to the other things I want to fix.

EDIT: Thanks to the help of the core PyPy developers we determined that the problem was that arrays allocated with the flavor “raw” were being accepted. Apparently these arrays still have length fields, by using rffi.CArray I was able to instruct PyPy to construct an array without a stored length field.

I’d also like to add that in the final hours, we added support for the NumPy __array_interface__ so that as soon as NumPy is working on PyPy, NumPy can take micronumpy arrays and do all sorts of useful things with them, and then when you need speed for simpler operations, you can pass your NumPy arrays to micronumpy (this side of the transaction hasn’t been implemented yet).

The End

So here we are at the end of the Summer of Code, and my project isn’t where I wanted it to be. Specifically, given my addition of slice support, performance has dropped to around 50% faster than NumPy, even farther from my goal, so that’s my top priority to address in the coming weeks. In my previous blog post I outlined what my plans are for the future (as I don’t like leaving things undone). Basically it comes down to:

  • Optimizing!
  • Minor compatibility fixes
  • Bridging NumPy and PyPy completely

Thanks

I’d just like to thank Google very quickly, and specifically Carol Smith, who has done a great job of managing the Google Summer of Code this year. I thoroughly enjoyed the program, and would love to do it again given the chance. I’ve learned a lot about writing software, dealing with deadlines, and time management (which is a skill I’ve let atrophy…) this summer. And thanks to you who’ve taken interest in my project. If you want to check back occasionally, the summer may be over, but my project isn’t, and I’ll be sure to brag about benchmark results as soon as they’re more favorable :-).

I’d also like to thank my mentor, Stéfan van der Walt for his help throughout my project, for being supportive and understanding when unexpected problems occurred and set us back. And I’d like to thank Maciej FijaŃkowski for his support from the PyPy side. The rest of the PyPy developers have all been helpful at some point or another, so thanks to them too.

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When All is Said and Done

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