On Jan 24, 2009, at 1:53 AM, joe wrote:
On Fri, Jan 23, 2009 at 2:36 AM, Leonardo Santagada <email@example.com
I don't think TraceMonkey is slower than V8 I believe the last time
I looked TraceMonkey was faster than V8 and it is becoming even faster at each interaction.
The way TraceMonkey works reminds me a bit of Psyco, although I
might be mixing it with the PyPy JIT. But talking about Psyco, why people
don't go help Psyco if all they want is a JIT? It is not like the idea of
having a JIT on Python is even new... Psyco was optimizing code even before
This leads me to believe that relatively simple,
concepts in VM design can have a bigger impact then specific, highly
complicated JIT solutions, in the context of dynamic languages that can't be
easily typed at compile time.
I believe in the exact oposite, and TraceMonkey is probably one of
the proofs of that...
Well, perhaps there's validity to both sides; V8 is still pretty fast. I didn't know they'd improved tracemonkey that much, interesting. Trace trees struck me as fairly different from psyco's design (it's been a while since I looked at the papers for both though); Psyco essentially was designed to optimized specific forms of code, while trace trees is more generalized.
Yes I confused the design of pypy jit with the psyco one, sorry.
So I've thought of a few ideas for a more (new)
This would modify the language, so it might be interesting, but would generate something which is not Python.
I don't think so. I didn't mean modify the python object model, I meant use a modified version of cpython's implementation of it. The cpython C API isn't part of the language standard, after all, and it's kindof inefficient and complex, imho.
I never messed much with the cpython implementation of it, but maybe.
If I remember correctly the idea for it is to be reasonably simple so
that it is easy to write extension modules and incorporate it with c
Don't know, but a good GC is way faster than what CPython is doing
already, but maybe it is a good idea to explore some others perspectives on
I disagree. I dislike GCs as a generalized solution, you don't always need the overhead of a full GC. Reference counting + a GC seems like a better compromise, since there's less work for it to do (or rather, the work is spread over time in smaller amounts).
A generational GC does exactly that, it spread the work of a
collection around, not as fine grained as refcounting but maybe
simpler to implement if in the end you also want to get rid of the
GIL... but yes this is still a point that is not a decided matter...
Maybe it would help a bit. I don't think it would help more than
10% tops (but I am completely guessing here)
Ah, 10% sounds like it would be worth it, actually. The simple code generation SquirrelFish does is interesting too, it essentially compiles the opcodes to native code. The code for simpler opcodes (and simple execution paths in the more complex ones) are inlined in the native code stream, while more complex opcodes are called as functions.
There is a tool on pypy to do this also, you should look at it (I
always forget the name, but it would be easy to find on pypy site).
The problem with this is (besides the error someone has already
stated about your phrasing) that python has really complex bytecodes, so this
would also only gain around 10% and it only works with compilers that accept
goto labels which the MSVC for example does not (maybe there are more
compilers that also doesn't).
Python's bytecode isn't all that complex, when I looked at it. It's not that much worse then Squirrelfish's original bytecode specification (which I need to look at again, btw, not sure what they're doing now). I was kindof surprised, thought it'd be much worse.
There was a discussion about this on pypy-dev only a week ago, you
might have some fun looking at the archives.
A form of hidden classes is already part of PyPy (but I think that
only the jit does this). But you can simply remove string lookups as people
can implement special methods to track this on the current Python. As I
said before I don't believe changing the semantics of python for the
sake of performance is even possible.
Obviously you'd have to go through the effort to not change the language semantics, which would mean still allowing things like __get/setattr__ and __get/setattribute__, a working __dict__, etc.
Thats one of the reason I think that a JIT is probably the only answer
for performance in python. At any time you could appen any of the
special methods to a class...
I'm not sure I'll have the time to anytime soon to
ideas, but I thought I'd kick them out there and see what people say. Note,
I'm in no way suggesting any sort of change to the existing cpython VM (it's
way, way too early for that kind of talk).
If you are not talking about changing CPython VM why not look at
Psyco and PyPy? :)
I looked at Psyco. It didn't look like it had much further potential to me. It only optimizes certain situations; it's not very generalized. PyPy looks interesting though.
Psyco optimizes a lot of situations, the next version I gathered is
going to optimize generator expressions, but yes, pypy I guess is the
long term answer to performance and python.
ps1: I thought you just forgot to send this to the list so I am
replying to it, hope you don't mind.
ps2: I'm not part of pypy core team or anything, so my view is just,
Leonardo Santagada santagada at gmail.com