After I ran the experimental evaluation, I had similar thoughts. If PyPy ever matches the current version of CPython I'm not sure why one wouldn't use PyPy over CPython. The biggest hurdle is matching support for popular libraries like NumPy, Tensorflow, Pandas, Scipy etc. I know they're working on supporting these, it's definitely a lot of work to do, easier said than done.

I am hoping Facebook to fund PyPy given that Instagram runs on Python.

It seems Google and Dropbox are not interested. Google is working on Grumpy, Dropbox worked on Pyston.

On Google's case it appears they see more worthwhile to migrate their Python code into Go and Swift than improving Python runtimes.

Remember Unladen Swallow?

Wouldn't the biggest issue still be that C modules either don't work or are slow? I'd imagine it's much better to be able to solve performance bottlenecks by using cython/c than to have overall faster runtime, but no option to go further.

Engineers usually have more fun rewriting everything in “that new shiny tool that people is speaking about”.

Managers enjoy avoiding conflicts.

Very rarely someone in a position of power will point out to this kind of solution, which anyway is going to be against wishes of many employees.

The Python ecosystem in general is severely underfunded despite all big players using it extensively.

I think one reason is that the community is doing too good of a job. The language is pretty sane, it solves most problems right, the libs and docs are good, and the general direction thinks take is reasonable. And it's free not only as beer and freedom, but also free from business influences. The PSF is really giving away pretty much everything.

Everybody contribues a little (we have the brett canon team from ms, the guido team from dropbox, the alexi martelli team from google, mozilla even donated for pypy, etc). But it's nothing massive. Nobody said "ok here is 10 millions euros, solve the packaging problem".

Compare to JS: the language started as slow, with terrible design, and no consensus on the direction to take. So eventually, people (Google first) pourred a load of money to it until it became usable, and they had a cleaner leadership. They had huge problem to solve on the ever expending market that is the web plateform. Of course JS as the unfair advantage of a captive audience and total monopoly on its field.

Remember Unladen shallow ? "Google" attempt to JIT Python ? It was just one guy during his internship (http://qinsb.blogspot.fr/2011/03/unladen-swallow-retrospecti...).

And look at the budget the PSF had in 2011 to help the community: http://pyfound.blogspot.fr/2012/01/psf-grants-over-37000-to-... I mean, even today they have to go though so many shenanigans for barely 20k (https://www.python.org/psf/donations/2018-q2-drive/).

But at the same time you hear people complaining they yet can't migrate to Python 3 because they have millions of lines of Python. You hear of them when they want to extend the support for free, but never to support the community.

It's ridiculous.

Also compare to PHP: the creators made a business out of it, plain and simple.

Compare to Java/C#/Go: it's owned by huge players that have a lot of money engaged.

Python really needs a sugar daddy so that we can tackle the few items remaining on the list:

- integrated steps to make an exe/rpm/deb/.app

- JIT that works everywhere

- mobile dev

- multi-core with fast and safe memory sharing

There are projects for that (nuikta, pyjion, kivi, etc), but they all lack of human power, money and hence integration, perfs, features, etc.

You need a simple way to code some GUI, make it work on mobile or desktop, turn it into and exe and distribute it.

You need a simple way to say "this is a long running process, JIT the hell out of it".

Regarding the bytecode - it was always considered an internal implementation detail subject to change (unlike the JVM bytecode) and in 3.6 they have in fact made a fairly major change[1]:

"The Python interpreter now uses a 16-bit wordcode instead of bytecode which made a number of opcode optimizations possible."

They haven't been shy about changing it in the past either, since there's no guarantee of stability, so it's likely to continue to change in incompatible ways.

[1] https://docs.python.org/3/whatsnew/3.6.html#optimizations

> you look up local variables in hash tables every time they are referenced. This is not what Python does: It maps variable names to integer indices during compilation to bytecode, and the bytecode just takes those embedded constant indices and indexes into an array to obtain a local variable's value.

This is only true for function arguments right? Module level bindings and class and object attributes are looked up in dictionaries. I think the same for variables used in closures too?

> the work didn't really go anywhere;

That's really too bad.

> there were no really obvious optimizations to try based on the data.

Is that because Python already is the way it is? In other words, if you started from scratch, how would you design a language differently so that it doesn't run into these issues?

Asking for a friend ;-)

That work is great!

> We have presented the first limit study that tries to quantify the costs of various dynamic language features in Python.

This is spot on what we were doing as well, that's great to have this as a reference.

> 1. If I understand the source on GitHub correctly, you parse Python source code yourself. I'm fairly sure your simulation would be a lot more faithful if you compiled Python bytecode instead. Did you consider this, and if yes, was there a particular reason not to do it that way?

We did not consider this actually. This would be a very interesting concept to explore. For the unoptimized version of Cannoli we do look up variables in a list of hash tables (which represent the current levels of scope). We did perform a scope optimization that then uses indices to access scope elements and this was much faster. However, it meant that the use of functions like `exec` and `del` were no longer permitted since we would not be able to statically determine all scope elements at run time (consider `exec(input())`, this could introduce anything into scope and we can't track that).

If you know, how does CPython resolve scope if it maps variable names to indices? In the case of `exec(input())` and say the input string is `x = 1`, how would it compile bytecode to allocate space for x and index into the value? I don't have much experience with the CPython source, so please excuse me if the question seems naive :)!

> 2. Where do you actually make useful use of Rust's static ownership system? I've only skimmed that part of the thesis very quickly, but I missed how you track ownership in Python programs and can be sure that things don't escape. Can you give an example of a Python program using dynamic allocation that your compiler maps to Rust with purely static ownership tracking and freeing of the memory when it's no longer used?

Elements of the Value enum (that encapsulates all types) relied on `Rc` and `RefCell` to defer borrow checking to run time. Consider a function who has a local variable that instantiates some object. Once that function call has finished Cannoli will pop that local scope table and all mappings will be dropped when it goes out of scope. The object encapsulated in a `Rc` will have it's reference count decremented to 0 and be freed.

This is how I've interpreted the Rust borrow checker, I will say that this was the first time I had ever used Rust so it's possible that I am not completely right on this. But once that table goes out of scope, all elements should be dropped by the borrow checker and any Rc should be decremented/dropped.

> 3. Related to 2: Why bother with any notion of ownership at all? Did you try mapping everything to Rust's reference counting and just letting it do its best? I'm wondering how much slower that would be. Python is also reference counted, after all, and I guess the Rust compiler should have more opportunities to optimize reference counting operations.

I did defer a lot of borrow checking to run time with Rc, but I tried to use this as little as possible to maximize optimizations that may result from static borrow checking.

> 4. In general, do you have an idea why your code is slower than Python, besides the hash table variable lookup issue I mentioned above?

If you remove the 3 outlier benchmarks (that are slow because of Rust printing and a suboptimal implementation of slices), Cannoli isn't too far off from CPython. And in fact, with the ray casting benchmark, Cannoli began to outperform CPython at scale. This leads me to believe that the computations in Cannoli are faster than CPython. However, there is still a lot of work to do to create a more performant version of Cannoli. The compiler itself was only developed for ~4 months, I have no doubt that more development time would yield a better results.

That being said, I think the biggest slowdown comes from features of Rust that might not have been utilized. This is just speculation, but I think the use of lifetimes could benefit the compiled code a lot. I also think there may be more elegant solutions to some of the translations (e.g. slices), that could provide speedup. But I can't say that there is one thing causing the slowdown, and profiling the benchmarks (excluding the outliers) support that.

As others have commented, AOT compilation is limited to the information available at compile time. Various features of Python like dynamic typing and object/class mutation (via del) preclude many static analysis techniques. In Cannoli, this meant that the compiler had to also generate code that manages scope at run time. Whenever an identifier was encountered in the compiled code a hashmap would be searched to find the bound value. This overhead becomes expensive, and the thesis covers optimizations that avoid this. PyPy's JIT operates on the PyPy interpreter itself, finding linear lists of operations that are frequently used. It can then compile these operations to bytecode so the next time that trace is encountered it can execute the compiled code. The self-analysis at run time provides information that an AOT compiler just doesn't have.

That being said, I did leave a few suggestions in the "future work" section that talk about writing an AOT compiler for RPython (the version of Python that PyPy's interpreter is written in). This would provide more information at compile time and would be an interesting comparison between a Python interpreter compiled AOT versus a Python interpreter with a JIT (PyPy).

Compiling to machine code is not a panacea for optimization. A optimized JIT compiler is going to blow an AOT compiler out of the water. Being smart about the machine code generated is significantly more important than generating machine code. In particular, PyPy makes several optimizations over python code that a more direct implementation of CPython at the machine level probably wouldn't. For example, PyPy erases dictionary lookups for object member access if the object shape is statically known. Given how prevalent this kind of lookup is in Python code, it's possible that even an interpreter that made this optimization would be faster than a machine code version that used an actual hash table.

I think this compiler also makes this particular optimization, but this is just one of many many optimizations PyPy does. I imagine that with sufficient work, this compiler could be brought up to speed with PyPy, but as it stands right now, PyPy simply benefits from having years of optimization work that a new project doesn't.

For most dynamic languages, the available speedups aren't in simple compilation, but in removing the runtime type checks, method lookups, and other slow operations. This needs the ability to guess at what the code is going to do based on past behavior, and generate specialized versions that get thrown away if the guesses are invalidated.

So, for example, you might see that the last 100 calls to a function were done with integers, so you can generate a variant of the function that only works for integers, and check if it's applicable when you enter the function. If that function stops getting used, you can throw it away.

Doing that well ahead of time requires an extremely good idea of how the program will behave at run time, and even with good information, is still very likely to bloat up your binary hugely. (Facebook used to compile their PHP codebase to a multi-gigabyte binary before moving to HHVM, for example).

JITs get to analyze both code and data and optimize for each machine deployed to. A static compiler can only analyze code and the machine used for compilation. If dependencies were pre-compiled, the static compiler won't be able to optimize their relationship with the project. If the machine is changed for deployment.

More information means better optimizations. JITs FTW.

Does it claim better performance than CPython or PyPy? I can't quite find the reference to PyPy (after a quick scan of the page/github repo. It looks like a cool project! They seem to be doing a lot of optimizations, which they list on their github page https://github.com/kayhayen/Nuitka#optimization. It looks like the git repo was created ~2013 (I dunno if it was hosted/worked-on elsewhere prior to that) so they've had a few years to optimize. Cool project though!

It doesn't claim that at all.

It can be, but "inherently" is a bit strong. There's also the question of "the best Rust programmer vs the best C programmer" vs "the average Rust programmer" vs the "average C programmer" here too.

Probably never. This is a subset of Python that would break most libraries.

The author says that this is a research project, and adding the standard library (if possible at all) would be a humongus task by itself.

This is already quite possible! There are even multiple libraries to help you get started. Extending languages like this is a huge use case for Rust; one of the first production Rust uses was extending Ruby like this.

Well, a professional software developer should be at least on the same level - and these comments are made by professionals.

Well, yes I see your point. But I wouldn't say that it was easy, just not as bad as it had been hyped up to be. Although I think most of that sentiment comes from older Rust versions, when lifetimes were defined in more places and you had to learn some of the more advanced concepts.

Well, it is a fun name.

Do you plan to support this down the line? I feel like compilers that compile down to Rust could become really good tools depending on how the popularity of the language goes.

Took me a minute too. "pie crust".

Fortunately, that means the "pie crust" name is still available for a Pike[1]†-Rust project :-)

[1]: https://en.wikipedia.org/wiki/Pike_(programming_language) †: I have a particular fondness for Pike, because it's derived from the first programming language I ever used: LPC. Anyone else an LPC hacker?

Ah, I hadn't realized that Cannoli is also using Rust-style memory management. In that case compiling to Rust would certainly help a lot.