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Commit 90111475 authored by Armin Rigo's avatar Armin Rigo
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Rewrite and extend. Please review!

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One of the goals of the PyPy project is to provide a fast and compliant python
interpreter. Part of the way we achieve this is to provide a high-performance
garbage collector and a high performance JIT. Results of comparing PyPy and
CPython can be found on the `speed website`_. Those benchmarks are not a random
collection. They're a combination of real-world Python programs, benchmarks
originally included and benchmarks we found PyPy to be slow on. Consult
descriptions of each for details.
One of the goals of the PyPy project is to provide a fast and compliant
python interpreter. Part of the way we achieve this is to provide a
high-performance garbage collector (GC) and a high-performance
Just-in-Time compiler (JIT). Results of comparing PyPy and CPython can
be found on the `speed website`_. Those benchmarks are not a random
collection: they are a combination of real-world Python programs,
benchmarks originally included with the (now dead) Unladen Swallow
project, and benchmarks we found PyPy to be slow on (and improved).
Consult the descriptions of each for details.
......@@ -8,7 +10,12 @@
JIT is however not a magic bullet. There are several characteristics that might
be surprising for people having first encounter with it. JIT is generally good
at speeding up straightforward python code that spends a lot of time in
the bytecode dispatch loop, numerics, heave oo etc. When JIT does not help,
PyPy is generally slower than CPython, those things include:
The JIT is however not a magic bullet. There are several characteristics
that might be surprising for people that are not used to JITs in
general, or to the PyPy JIT in particular. The JIT is generally good at
speeding up straightforward Python code that spends a lot of time in the
bytecode dispatch loop, i.e. running actual Python code --- as opposed
to running things that are only invoked by your Python code. Good
examples include numeric calculations or any kind of heavily
object-oriented program. Bad examples include doing computations with
large longs --- which is done by unoptimizable support code. When the
JIT does not help, PyPy is generally slower than CPython.
......@@ -14,5 +21,7 @@
* **Tests**: Ideal unit tests would execute each piece of tested code which
leaves no time for the JIT to warm up.
In more details, the JIT is known not to work on:
* **Tests**: The ideal unit tests execute each piece of tested code
once. This leaves no time for the JIT to warm up.
* **Really short-running scripts**: A rule of thumb is if something runs below
......@@ -17,6 +26,8 @@
* **Really short-running scripts**: A rule of thumb is if something runs below
0.2s JIT has no chance, but it depends a lot on the program in question. In
general, make sure you warm up your program before running benchmarks if
you're measuring something long-running like a server.
0.2s the JIT has no chance, but it depends a lot on the program in question.
In general, make sure you warm up your program before running benchmarks, if
you're measuring something long-running like a server. The time required
to warm up the JIT varies; give it at least a couple of seconds. (PyPy's
JIT takes a specially long time to warm up.)
......@@ -22,4 +33,5 @@
* **Functions in runtime**: Functions that take significant time in runtime.
PyPy's runtime is generally not as optimized as CPython's and expect those
* **Long-running runtime functions**: These are the functions provided
by the runtime of PyPy that do a significant amount of work.
PyPy's runtime is generally not as optimized as CPython's and we expect those
functions to take somewhere between same time as CPython to 2x longer.
......@@ -25,3 +37,5 @@
functions to take somewhere between same time as CPython to 2x longer.
XXX explain exactly what runtime is
This includes for example computing with longs, or sorting large lists.
A counterexample is regular expressions: although they take time, they
come with their own JIT.
......@@ -27,4 +41,4 @@
Unrelated things that we know PyPy is slow at (note that we're probably working
on it):
Unrelated things that we know PyPy to be slow at (note that we're probably
working on it):
......@@ -30,7 +44,5 @@
* **Long integers**
* **Building very large dicts**
* CPython C extension modules
* **Building very large dicts**: This is so far an issue with our GCs.
Building large lists works much better; the randomness of order in
dictionaries is what hurts performance right now.
......@@ -36,3 +48,11 @@
XXX
* **CPython C extension modules**: Any C extension module recompiled
with PyPy takes a very large hit in performance. The purpose of being
able to compile C extension modules with PyPy is for them to work at
all. If the extension module is for speedup purposes only, then it
makes no sense to have it on top of PyPy right now. Just remove it
and use a straightforward Python version. If the extension module is
both performance-critical and an interface to some C library, then it
might be worthwhile to consider rewriting it as a pure Python version,
using something like ``ctypes``.
......@@ -38,7 +58,7 @@
We generally consider things that are slower on PyPy than CPython PyPy's bugs.
In case you find a thing that's not documented here, report it to our
`bug tracker`_ for investigation
We generally consider things that are slower on PyPy than CPython bugs
of PyPy. In case you find some issue that is not documented here,
please report it to our `bug tracker`_ for investigation.
.. _`bug tracker`: http://bugs.pypy.org
.. _`speed website`: http://speed.pypy.org
......
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