16 ms·

I don't know, looks like ~15% improvement [1]. Doubt the Mojo guys are going to lose much sleep (they claimed 35,000x improvement). [1] https://speed.python.or

• TwentyPosts 3y ago
  Somehow I'm still skeptical! Feels like "strict superset of Python, all Python code works" and "several orders of magnitude faster" just sounds like you're trying to have your cake and eat it, too. I doubt that the Mojo developers have some sort of 'secret sauce' or 'special trick' that will get them there. And even if they have something, I don't see why the Python devs wouldn't just implement the same approach, considering they're currently trying to make Python faster. I assume that (as long as Mojo wants to stick to its goal of being a strict superset of Python), there will be a lot of things that just cannot be 'fixed'. For example, I'd be surprised if Python's Global Interpreter Lock isn't entangled with the language in a few nasty ways that'd make it really difficult to replace or improve upon (while retaining compatibility). Then again, the developer of Swift is working on it, right? I guess he's got the experience, at least.
  • School-Cotton 3y ago
    IDK about 35,000x but Python really is outrageously slow relative to other popular languages, even other interpreted ones. It's more comparable to something like Ruby than to something like JavaScript.
    • williamstein 3y ago
      From their FAQ: "How compatible is Mojo with Python really? Mojo already supports many core features of Python including async/await, error handling, variadics, etc, but… it is still very early and missing many features - so today it isn’t very compatible. Mojo doesn’t even support classes yet!". Overall, pure Python seems to be about 100x slower than what you can reasonably get with a compiled language and some hard work. It's about 10x slower than what you can get from JITs like Pypy and Javascript, when such comparisons makes sense. I agree that Mojo remind me of Cython, but with more marketing and less compatibility with Python. Cython aspired to be a nearly 99% superset of Python, at least that's exactly what I pushed my postdocs and graduate students to make it be back in 2009 (e.g., there were weeks of Robert Bradshaw and Craig Citro pushing each other to get closures to fully work). Mojo seems to be a similar modern idea for doing the same sort of thing. It could be great for the ecosystem; time will tell. Cython could still also improve a lot too -- there is a major new 3.0 release just around the corner!: https://pypi.org/project/Cython/#history https://pypi.org/project/Cython/#history
      • deleted 3y ago
        [deleted]
  • wiseowise 3y ago
    Isn’t Mojo compiled? While Python needs to be interpreted.
    • TwentyPosts 3y ago
      Mojo bothers offers Just-In-Time and Ahead-Of-Time models. Proper AOT compilation is going to offer a significant speedup, but probably not enough to get to their stated goals. And good luck carrying all of Python's dynamic features across the gap.
    • mikepurvis 3y ago
      I think the point is that it's really hard to get those gains from "being compiled" when every function or method call, every use of an operator, everything is subject to multiple levels of dynamic dispatch and overrideability, where every variable can be any type and its type can change at any time and so has to be constantly checked. A language that's designed to be compiled doesn't have these issues, since you can move most of the checking and resolution logic to compile-time. But doing that requires the language's semantics to be aligned to that goal, which Python's most certainly are not. The achievements of Pypy are pretty incredible in the JIT space, but the immense effort that has gone in there definitely is a good reason to be skeptical of Mojo's claims of being both compiled and also a "superset" of Python.
      • waterhouse 3y ago
        So you want a system that figures out that the vast majority of function and method calls are not overridden, that these variables and fields are in practice always integers, etc.; generates some nice compiled code, and adds some interlock so that, if these assumptions do change, the code gets invalidated and replaced with new code (and maybe some data structures get patched). I think that this kind of thing is in fact done with the high-performing Javascript engines used in major browsers. I imagine PyPy, being a JIT, is in a position to do this kind of thing. Perhaps Python is more difficult than Javascript, and/or perhaps a lot more effort has been put into the Javascript engines than into PyPy?
        • deleted 3y ago
  • Conscat 3y ago
    Well, they do have MLIR, which is probably the closest thing to "secret sauce" they've got. I'm excited by some of the performance-oriented features like tiling loops, but they'll need the multitude of optimization-hints that GCC and Clang have, too. They also have that parallel runtime, which seems similar to Cilk to me, and Python fundamentally can never have that as I understand it.
  • pavon 3y ago
    I am a little skeptical as well, but I do think there is a lot of areas for improvement beyond the changes going in mainline. Note that while Mojo intends to support all of python, they never claimed that it will still be fast if you make heavy use of all the dynamic features. The real limiting factors are how often those dynamic features are used, and how frequently the code needs to be checking whether they are used. The fast CPython changes are still very much interpreter-centric, and are checking whether assumptions have changed on every small operation. It seems to me that if you are able to JIT large chunks of code, and then push all the JIT invalidation checks into the dynamic features that break your assumptions rather than in the happy path that is using the assumptions, you ought to be able to get much closer to to Javascript levels of performance when dynamic features aren't being used. Then support for those dynamic features becomes a fallback way of having a huge ecosystem from day one, even if it is only modestly faster than CPython.
  • spprashant 3y ago
    The way I read it, Python code as it is, won't see a huge bump it's like 10x or something. You have to use special syntax and refactoring to get the 1000x speed ups. The secret sauce is essential a whole new language within the language which likely helps skip the GIL issues.
    • jck 3y ago
      Do they claim that existing python code would get a 10x bump? That sounds too good to be true
  • mirekrusin 3y ago
    ...Swift and creator of LLVM and Clang.
• tgma 3y ago
  That comparison seems quite cherry picked. Unlikely that it is generalizable. In my tests with Mojo, it doesn’t seem to behave like Python at all so far. Once they add the dynamism necessary we can see where they land. I’m still optimistic (mostly since Python has absolute garage performance and there’s low hanging fruit) but it’s no panacea. I feel their bet is not to have to run Python as is but have Python developers travel some distance and adapt to some constraints. They just need enough momentum to make that transition happen but it’s a transition to a distinct language, not a Python implementation. Sort of what Hack is to PHP.
  • SOLAR_FIELDS 3y ago
    Hack as a comparison is probably not a great thing for Mojo, since no one outside of Facebook uses Hack.
    • tgma 3y ago
      Safe to say no one outside of Modular uses Mojo either at the moment.
• KeplerBoy 3y ago
  I don't buy the mojo hype. The concept of compiling python has been tried again and again. It has its moments, but anything remotely important is glued in from compiled code anyways.
  • ptx 3y ago
    From a quick reading of the Mojo website, it sounds like gluing in compiled code is exactly what they're doing, except this time the separate compiled language happens to look sort of like Python a bit. For the actual Python code it still uses CPython, so that part doesn't get any faster.
    • morelisp 3y ago
      Pyrex, Cython, mypyc, Codon... we've been down this road before plenty too. If Mojo succeeds it will be purely based on quality of implementation, not a spark of genius.
      • pdpi 3y ago
        The "spark of genius" might very well be a novel implementation strategy.
        • neolefty 3y ago
          It leverages the Multi-Level Intermediate Representation (MLIR) https://mlir.llvm.org/ https://mlir.llvm.org/ which is a follow-on from LLVM, with lots of lessons learned: https://www.hpcwire.com/2021/12/27/lessons-from-llvm-an-sc21-fireside-chat-with-chris-lattner/ https://www.hpcwire.com/2021/12/27/lessons-from-llvm-an-sc21...
          • morelisp 3y ago
            Again, "compile to an IR" isn't exactly ground-breaking. The devil will be in the details.
      • deleted 3y ago
• klyrs 3y ago
  I've been rewriting Python->C for nearly 20 years now. The expected speedup is around 100x, or 1000x for numerical stuff or allocation-heavy work that can be done statically. Whenever you get 10,000x or above, it's because you've written a better algorithm. You can't generalize that. A 35k speedup is a cool demo but should be regarded as hype.
  • nomel 3y ago
    > The expected speedup is around 100x, or 1000x for numerical stuff What if you stay in the realm of numpy? What's the biggest offender that you see?
    • klyrs 3y ago
      > What if you stay in the realm of numpy? You mean, what if you're only doing matrix stuff? Then it's probably easier to let numpy do the heavy lifting. You'll probably take less than a 5x performance hit, if you're doing numpy right. And if you're doing matrix multiplication, numpy will end up faster because it's backed by a BLAS, which mortals such as myself know better than to compete with. > What's the biggest offender that you see? Umm... every line of Python? Member access. Function calls. Dictionaries that can fundamentally be mapped to int-indexed arrays. Reference counting. Tuple allocation. One fun exercise is to take your vanilla python code, compile it in Cython with the -a flag to produce an HTML annotation. Click on the yellowest lines, and it shows you the gory details of what Cython does to emulate CPython. It's not exactly what CPython is doing (for example, Cython elides the virtual machine), but it's close enough to see where time is spent. Put the same code through the python disassembler "dis" to see what virtual machine operations are emitted, and paw through the main evaluation loop [1]; or take a guided walkthrough at [2]. [1] https://github.com/python/cpython/blob/v3.6.14/Python/ceval.c#L1274 https://github.com/python/cpython/blob/v3.6.14/Python/ceval.... (note this is an old version, you can change that in the url) [2] https://leanpub.com/insidethepythonvirtualmachine/read https://leanpub.com/insidethepythonvirtualmachine/read
      • vbarrielle 3y ago
        Due to the possibility to fuse multiple operations in C++ (whereas you often have intermediate arrays in numpy), I routinely get 20x speedups when porting from numpy to C++. Good libraries like eigen help a lot.
• ehsankia 3y ago
  35,000x suddenly becomes 30,000x, not as impressive!
• Alex3917 3y ago
  How does that reconcile with the benchmarks here, which say that Python 2.12 is currently somewhere between 5% slower to 5% faster? https://github.com/faster-cpython/benchmarking-public https://github.com/faster-cpython/benchmarking-public
• make3 3y ago
  if it's an improvement that big it needs to be with GPUs, & gpus can be used normally with torch etc