Is it possible to extend CPython with pypy to make use of the C modules in CPython and to write fast number crunching calculations in PyPy for its convince and ease of use. If so how can it be done?
I have searched the web throughly before asking to make sure i do not waste anyone's time but extending CPython with PyPy sounds like a good idea.
Related
I need pypy to speed up my python code. While the pypy doesn't support a lot of modules I need (e.g. GNU Radio). Could I use pypy to speed up parts of my python code. Could I use pypy to only speed up some of my python files? How can I do that?
No, you can't. You can only have one interpreter instance running all of the code in a single program at a time. The exception is if you break out some of your functionality into a totally separate program that communicates with the other part of your code through some form of inter-process communication; then you can run those totally separate programs however you like. But for code that is not separated like that, it's not possible.
It will probably be more straightforward to adapt the entirety of your code to work with PyPy one way or another, instead of trying to break out bits and pieces. If that's absolutely not possible, then PyPy probably can't help you.
No, you can't. And GNU Radio does the signal processing and scheduling in C++, so that's totally opaque to your python interpreter. Also, GNU Radio itself is highly optimized and contains specialized implementations for most of the CPU intense tasks for SSE, SSE4, and some NEON.
I need pypy to speed up my python code.
I doubt that. If your program runs too slow, it's probably nothing your Python interpreter can solve -- you might have to look into what could take so much time, and solve this on a higher level.
I'll preface this by saying I am quite new to PyPy, though fairly experienced with Python.
I'm looking to run a web app where I run untrusted Python code. The PyPy sandboxing features look ideal for what I'm doing.
The PyPy docs on sandboxing indicate that you can call a PyPy sandbox from either Python or PyPy. This seems to imply that there's some separate program or executable that is the sandbox.
I'm wondering, is it possible to call a PyPy sandbox from a non-Python language? I'm looking at Haskell in particular, but it's also very possible that I could use C or C++ as an intermediate.
Yes, that's possible. The PyPy sandbox is a separate process communicating only via stdin/stdout. If you want to rewrite the "external" part, you can; it's not using anything that should be too heavily Python-related.
Note that the sandboxing feature of PyPy is not being maintained any more, see http://www.pypy.org/features.html#sandboxing
I was looking at PyPy and I was just wondering why it hasn't been adopted into the mainline Python distributions. Wouldn't things like JIT compilation and lower memory footprint greatly improve the speeds of all Python code?
In short, what are the main drawbacks of PyPy that cause it to remain a separate project?
PyPy is not a fork of CPython, so it could never be merged directly into CPython.
Theoretically the Python community could universally adopt PyPy, PyPy could be made the reference implementation, and CPython could be discontinued. However, PyPy has its own weaknesses:
CPython is easy to integrate with Python modules written in C, which is traditionally the way Python applications have handled CPU-intensive tasks (see for instance the SciPy project).
The PyPy JIT compilation step itself costs CPU time -- it's only through repeated running of compiled code that it becomes faster overall. This means startup times can be higher, and therefore PyPy isn't necessarily as efficient for running glue code or trivial scripts.
PyPy and CPython behavior is not identical in all respects, especially when it comes to "implementation details" (behavior that is not specified by the language but is still important at a practical level).
CPython runs on more architectures than PyPy and has been successfully adapted to run in embedded architectures in ways that may be impractical for PyPy.
CPython's reference counting scheme for memory management arguably has more predictable performance impacts than PyPy's various GC systems, although this isn't necessarily true of all "pure GC" strategies.
PyPy does not yet fully support Python 3.x, although that is an active work item.
PyPy is a great project, but runtime speed on CPU-intensive tasks isn't everything, and in many applications it's the least of many concerns. For instance, Django can run on PyPy and that makes templating faster, but CPython's database drivers are faster than PyPy's; in the end, which implementation is more efficient depends on where the bottleneck in a given application is.
Another example: you'd think PyPy would be great for games, but most GC strategies like those used in PyPy cause noticeable jitter. For CPython, most of the CPU-intensive game stuff is offloaded to the PyGame library, which PyPy can't take advantage of since PyGame is primarily implemented as a C extension (though see: pygame-cffi). I still think PyPy can be a great platform for games, but I've never seen it actually used.
PyPy and CPython have radically different approaches to fundamental design questions and make different tradeoffs, so neither one is "better" than the other in every case.
For one, it's not 100% compatible with Python 2.x, and has only preliminary support for 3.x.
It's also not something that could be merged - The Python implementation that is provided by PyPy is generated using a framework they have created, which is extremely cool, but also completely disparate with the existing CPython implementation. It would have to be a complete replacement.
There are some very concrete differences between PyPy and CPython, a big one being how extension modules are supported - which, if you want to go beyond the standard library, is a big deal.
It's also worth noting that PyPy isn't universally faster.
See this video by Guido van Rossum. He talks about the same question you asked at 12 min 33 secs.
Highlights:
lack of Python 3 compatibility
lack of extension support
not appropriate as glue code
speed is not everything
After all, he's the one to decide...
One reason might be that according to PyPy site, it currently runs only on 32- and 64-bit Intel x86 architecture, while CPython runs on other platforms as well. This is probably due to platform-specific speed enhancements in PyPy. While speed is a good thing, people often want language implementations to be as "platform-independent" as possible.
I recommend watching this keynote by David Beazley for more insights. It answers your question by giving clarity on nature & intricacies of PyPy.
In addition to everything that's been said here, PyPy is not nearly as rock solid as CPython in terms of bugs. With SymPy, we've found at about a dozen bugs in PyPy over the past couple of years, both in released versions and in the nightlies.
On the other hand, we've only ever found one bug in CPython, and that was in a prerelease.
Plus, don't discount the lack of Python 3 support. No one in the core Python community even cares about Python 2 any more. They are working on the next big things in Python 3.4, which will be the fifth major release of Python 3. The PyPy guys still haven't gotten one of them. So they've got some catching up to do before they can start to be contenders.
Don't get me wrong. PyPy is awesome. But it's still far from being better than CPython in a lot of very important ways.
And by the way, if you use SymPy in PyPy, you won't see a smaller memory footprint (or a speedup either). See https://bitbucket.org/pypy/pypy/issues/1447/.
Does PyPy work with Py2Exe? If it doesn't, are there any similar solutions?
The answer is no (would be possible to get it running, but hard)
I'm pretty sure the answer is "no". py2exe appears to depend on CPython's C API, which I'm pretty sure PyPy doesn't support. (That is, I can't see why PyPy would support the CPython API, nor did I see any mention it in any of the obvious places on their website...)
From the Google Open Source Blog:
PyPy is a reimplementation of Python
in Python, using advanced techniques
to try to attain better performance
than CPython. Many years of hard work
have finally paid off. Our speed
results often beat CPython, ranging
from being slightly slower, to
speedups of up to 2x on real
application code, to speedups of up to
10x on small benchmarks.
How is this possible? Which Python implementation was used to implement PyPy? CPython? And what are the chances of a PyPyPy or PyPyPyPy beating their score?
(On a related note... why would anyone try something like this?)
"PyPy is a reimplementation of Python in Python" is a rather misleading way to describe PyPy, IMHO, although it's technically true.
There are two major parts of PyPy.
The translation framework
The interpreter
The translation framework is a compiler. It compiles RPython code down to C (or other targets), automatically adding in aspects such as garbage collection and a JIT compiler. It cannot handle arbitrary Python code, only RPython.
RPython is a subset of normal Python; all RPython code is Python code, but not the other way around. There is no formal definition of RPython, because RPython is basically just "the subset of Python that can be translated by PyPy's translation framework". But in order to be translated, RPython code has to be statically typed (the types are inferred, you don't declare them, but it's still strictly one type per variable), and you can't do things like declaring/modifying functions/classes at runtime either.
The interpreter then is a normal Python interpreter written in RPython.
Because RPython code is normal Python code, you can run it on any Python interpreter. But none of PyPy's speed claims come from running it that way; this is just for a rapid test cycle, because translating the interpreter takes a long time.
With that understood, it should be immediately obvious that speculations about PyPyPy or PyPyPyPy don't actually make any sense. You have an interpreter written in RPython. You translate it to C code that executes Python quickly. There the process stops; there's no more RPython to speed up by processing it again.
So "How is it possible for PyPy to be faster than CPython" also becomes fairly obvious. PyPy has a better implementation, including a JIT compiler (it's generally not quite as fast without the JIT compiler, I believe, which means PyPy is only faster for programs susceptible to JIT-compilation). CPython was never designed to be a highly optimising implementation of the Python language (though they do try to make it a highly optimised implementation, if you follow the difference).
The really innovative bit of the PyPy project is that they don't write sophisticated GC schemes or JIT compilers by hand. They write the interpreter relatively straightforwardly in RPython, and for all RPython is lower level than Python it's still an object-oriented garbage collected language, much more high level than C. Then the translation framework automatically adds things like GC and JIT. So the translation framework is a huge effort, but it applies equally well to the PyPy python interpreter however they change their implementation, allowing for much more freedom in experimentation to improve performance (without worrying about introducing GC bugs or updating the JIT compiler to cope with the changes). It also means when they get around to implementing a Python3 interpreter, it will automatically get the same benefits. And any other interpreters written with the PyPy framework (of which there are a number at varying stages of polish). And all interpreters using the PyPy framework automatically support all platforms supported by the framework.
So the true benefit of the PyPy project is to separate out (as much as possible) all the parts of implementing an efficient platform-independent interpreter for a dynamic language. And then come up with one good implementation of them in one place, that can be re-used across many interpreters. That's not an immediate win like "my Python program runs faster now", but it's a great prospect for the future.
And it can run your Python program faster (maybe).
Q1. How is this possible?
Manual memory management (which is what CPython does with its counting) can be slower than automatic management in some cases.
Limitations in the implementation of the CPython interpreter preclude certain optimisations that PyPy can do (eg. fine grained locks).
As Marcelo mentioned, the JIT. Being able to on the fly confirm the type of an object can save you the need to do multiple pointer dereferences to finally arrive at the method you want to call.
Q2. Which Python implementation was used to implement PyPy?
The PyPy interpreter is implemented in RPython which is a statically typed subset of Python (the language and not the CPython interpreter). - Refer https://pypy.readthedocs.org/en/latest/architecture.html for details.
Q3. And what are the chances of a PyPyPy or PyPyPyPy beating their score?
That would depend on the implementation of these hypothetical interpreters. If one of them for example took the source, did some kind of analysis on it and converted it directly into tight target specific assembly code after running for a while, I imagine it would be quite faster than CPython.
Update: Recently, on a carefully crafted example, PyPy outperformed a similar C program compiled with gcc -O3. It's a contrived case but does exhibit some ideas.
Q4. Why would anyone try something like this?
From the official site. https://pypy.readthedocs.org/en/latest/architecture.html#mission-statement
We aim to provide:
a common translation and support framework for producing
implementations of dynamic languages, emphasizing a clean
separation between language specification and implementation
aspects. We call this the RPython toolchain_.
a compliant, flexible and fast implementation of the Python_
Language which uses the above toolchain to enable new advanced
high-level features without having to encode the low-level
details.
By separating concerns in this way, our implementation of Python - and
other dynamic languages - is able to automatically generate a
Just-in-Time compiler for any dynamic language. It also allows a
mix-and-match approach to implementation decisions, including many
that have historically been outside of a user's control, such as
target platform, memory and threading models, garbage collection
strategies, and optimizations applied, including whether or not to
have a JIT in the first place.
The C compiler gcc is implemented in C, The Haskell compiler GHC is written in Haskell. Do you have any reason for the Python interpreter/compiler to not be written in Python?
PyPy is implemented in Python, but it implements a JIT compiler to generate native code on the fly.
The reason to implement PyPy on top of Python is probably that it is simply a very productive language, especially since the JIT compiler makes the host language's performance somewhat irrelevant.
PyPy is written in Restricted Python. It does not run on top of the CPython interpreter, as far as I know. Restricted Python is a subset of the Python language. AFAIK, the PyPy interpreter is compiled to machine code, so when installed it does not utilize a python interpreter at runtime.
Your question seems to expect the PyPy interpreter is running on top of CPython while executing code.
Edit: Yes, to use PyPy you first translate the PyPy python code, either to C and build with gcc, to jvm byte code, or to .Net CLI code. See Getting Started