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<div>
<div id="main">
<h1 class="title">Performance</h1>
<div class="contents topic" id="contents">
<p class="topic-title first">Contents</p>
<ul class="simple">
<li><a class="reference internal" href="#optimization-strategy" id="id1">Optimization strategy</a></li>
<li><a class="reference internal" href="#micro-tuning-tips" id="id2">Micro-tuning tips</a></li>
<li><a class="reference internal" href="#insider-s-point-of-view" id="id3">Insider's point of view</a></li>
</ul>
</div>
<p>This document collects strategies, tactics and tricks for making your
code run faster under PyPy. Many of these are also useful hints for
stock Python and other languages. For contrast, we also describe some
CPython (stock Python) optimizations that are not needed in PyPy.</p>
<div class="section" id="optimization-strategy">
<h1><a class="toc-backref" href="#id1">Optimization strategy</a></h1>
<p>These suggestions apply to all computer languages. They're here as
reminders of things to try before any Python or PyPy-specific tweaking.</p>
<div class="section" id="build-a-regression-test-suite">
<h2>Build a regression-test suite</h2>
<p>Before you start tuning, build a regression-test suite for your code.
This front-loads a significant amount of work, but it means you can
try lots of optimizations without worrying so much about introducing
functional bugs.</p>
</div>
<div class="section" id="measure-don-t-guess">
<h2>Measure, don't guess</h2>
<p>Human beings are bad at guessing or intuiting where the hotspots in code are.
Measure, don't guess; use a profiler to pin down the 20% of the
code where the code is spending 80% of its time, then speed-tune that.</p>
<p>Measuring will save you a lot of effort wasted on tuning parts of the code
that aren't actually bottlenecks.</p>
<p>As you tune, re-profile frequently so you can see how the hottest spots
are shifting around.</p>
</div>
<div class="section" id="i-o-bound-is-different-from-compute-bound">
<h2>I/O-bound is different from compute-bound</h2>
<p>Be aware of the difference between code that is compute-bound (slow
because it's doing a huge number of instructions) and code that is I/O
bound (slow because of disk or network delays).</p>
<p>Expect to get most of your gains from optimizing compute-bound code.
It's usually (though not always) a sign that you're near the end of
worthwhile tuning when profiling shows that the bulk of the
application's time is spent on network and disk I/O.</p>
</div>
<div class="section" id="tune-your-algorithms-first">
<h2>Tune your algorithms first</h2>
<p>Generally, when your code is doing things that are O(n**2) or larger
in the size of your data set, the cost of those operations is going
to swamp any small gains you can pick up with the tricks we describe
here.</p>
<p>Tune your algorithms first. It's time to think about applying our
list of micro-tuning tips <em>after</em> you think you've optimized out
intrinsically expensive operations.</p>
<p>That said, be prepared for the possibility that you will discover
better-hidden algorithmic problems as you micro-tune. Likely
you will go through this cycle more than once.</p>
</div>
<div class="section" id="focus-on-tight-loops">
<h2>Focus on tight loops</h2>
<p>It's extremely common for high time costs to lurk within some
innocuous-looking code inside a tight loop - especially in code
that does something like a searching/matching/lookup operation
or any kind of graph traversal.</p>
<p>Probably the most common kind of performance-killer in compute-bound
code is an O(n**2) operation that is disguised by being some sort of
O(n) lookup or match inside an O(n) loop.</p>
<p>Another common time-sink is relatively expensive common-setup
operations that are performed inside tight loops but could be moved
to before they start. (For a representative case of this, see the
micro-tuning tip on regexp compilation.)</p>
</div>
<div class="section" id="smaller-is-faster">
<h2>Smaller is faster</h2>
<p>Modern computers have multiple levels of memory caching, some directly
on the processor chip. Causing a cache miss at any level incurs a
performance penalty proportional to random-access time for the next
outward (and much slower) layer of cache.</p>
<p>Accordingly, smaller is faster. Programs or routines with a small
enough working set to fit inside a fast cache will be as fast as
that cache is. To make your code fast, reduce the length of the
series of Python or JIT-compiler opcodes it generates by making
it simpler.</p>
<p>The tradeoff here is that algorithmic tuning often trades time for
space - that is, it increases the size of an algorithm's working set
by including pre-computations or tables or reverse maps in order to
avoid O(n**2) operations.</p>
<p>It's impossible to predict in advance where the sweet spot in that
tradeoff will be. You have to try different things and measure -
which takes us right back to &ldquo;Measure, don't guess&rdquo;. And another
function of your regression test suite can be as a speed benchmark.</p>
</div>
</div>
<div class="section" id="micro-tuning-tips">
<h1><a class="toc-backref" href="#id2">Micro-tuning tips</a></h1>
<p>These are in no particular order.</p>
<div class="section" id="keep-it-simple">
<h2>Keep it simple</h2>
<p>Simple is better than complex. The PyPy JIT is not very smart; the
simpler your code is the better it will run. Here again, though, you face
a tradeoff: you may need to pay with more algorithmic complexity in order
to avoid brute-force operations that are O(n**2) or worse.</p>
<p>Write plain-vanilla code in plain-vanilla ways. The PyPy JIT has many
productions that optimize a common usage pattern against an uncommon
usage pattern.</p>
</div>
<div class="section" id="global-variables">
<h2>Global variables</h2>
<p>In CPython, global variables and functions (including package imports)
are much more expensive to reference than locals; avoid them. (This
is also good modularity practice).</p>
<p>The cost of CPython global references is high enough that, for example, if you
have code in a frequently-visited inner loop that uses int() a lot, it
may be worthwhile to create a local copy of the reference with &ldquo;int =
int&rdquo; in an enclosing block.</p>
<p>However, this in <em>not</em> true in JITted PyPy code. The &ldquo;int = int&rdquo; hack
won't buy you performance, it's just an extra copy. The modularity
reason for avoiding globals are still valid.</p>
</div>
<div class="section" id="regular-expressions">
<h2>Regular expressions</h2>
<p>Regular-expression compilation is expensive. If the regexp pattern in
a search, match, or replace operation is static (doesn't mutate at
runtime) refactor so it's only done once.</p>
<p>If the regexp compilation is in a class method, consider doing it as
the initializer of a regexp-valued static (shared) class member and
using that class member in your operation.</p>
<p>If the regexp compilation is in a free function, consider moving it
to module level and referencing the resulting regexp object
(but see the warning above about global variables).</p>
</div>
<div class="section" id="old-vs-new-style-classes">
<h2>Old- vs. new-style classes</h2>
<p>New-style classes allow faster attribute access and take up less core
per instance than old-style classes. Much of this advantage may be
lostm, however, if attribute names are not constant. For example: x.a
= y or even setattr(x, &lsquo;a&rsquo;, y) will be much faster than a dynamic
version: setattr(x, &lsquo;a&rsquo; + some_variable, y).</p>
<p>Classes that inherit from both new- and old-style classes are
<em>extremely</em> slow; avoid at all costs.</p>
<p>In PyPy, isinstance() called against an old-style class was very slow
until 2.0.</p>
</div>
<div class="section" id="string-concatenation-is-expensive">
<h2>String concatenation is expensive</h2>
<p>In CPython, you may want to replace:</p>
<pre class="literal-block">
s = head + body + maybe + tail
</pre>
<p>with the admittedly less readable:</p>
<pre class="literal-block">
s = &quot;%(head)%(body)%(maybe)%(tail)&quot; % locals()
</pre>
<p>or even:</p>
<pre class="literal-block">
s = &quot;{head}{body}{maybe}{tail}&quot;.format(locals())
</pre>
<p>Both of the latter forms avoid multiple-allocation overhead.
But PyPy's JIT makes the overhead of intermediate concatenations
go away in linear code that keeps the number of concatenations
small, bound and constant. (And <tt class="docutils literal">locals()</tt> is rather slow
with PyPy's JIT.)</p>
<p>On the other hand, in code like this with a string-valued foo() function:</p>
<pre class="literal-block">
for x in mylist:
s += foo(x)
</pre>
<p>the JIT cannot optimize out intermediate copies. This code is
actually quadratic in the total size of the mylist strings due to
repeated string copies of ever-larger prefix segments.</p>
<p>This:</p>
<pre class="literal-block">
parts = []
for x in mylist:
parts.append(foo(x))
s = &quot;&quot;.join(parts)
</pre>
<p>can be much faster because all the string concatenation in the last
line creates exactly one new string object with one C-level copy
sequence (and list operations are relatively cheap).</p>
</div>
<div class="section" id="frame-introspection-and-tracing-are-slow">
<h2>Frame introspection and tracing are slow</h2>
<p>Certain function calls can disable PyPy's speed options over
stretches of surrounding code called &ldquo;JIT scopes&rdquo;.</p>
<p>A JIT like PyPy's works based on the assumption that the only thing
worth optimizing are loops that are executed often. Whenever the
interpreter enters a loop in the interpreted program, the JIT records
what the interpreter does, creating a trace. This trace is optimized,
compiled to machine code and executed when the loop is hit with the
conditions observed during tracing. This trace is one kind of JIT scope.</p>
<p>Another kind of JIT scope that matters is a function, considered as
a unit for inlining.</p>
<p>Note that a JIT scope is a run-time phenomenon, not a compile-time
one. It's not confined by source-code module boundaries. A library-
or foreign-module call in a frequently-called loop or inlined function
will be part of its JIT scope.</p>
<p>locals(), globals(), sys._getframe(), sys.exc_info(), and sys.settrace
work in PyPy, but they incur a performance penalty that can be huge by
disabling the JIT over the enclosing JIT scope.</p>
<p>One unobvious case where frame introspection is used is the logging
module. Don't use the logging module if you need to run fast.</p>
<p><em>(Thanks Eric S. Raymond for the text above)</em></p>
</div>
</div>
<div class="section" id="insider-s-point-of-view">
<h1><a class="toc-backref" href="#id3">Insider's point of view</a></h1>
<p>This section describes performance issues from the point of view of
insiders of the project; it should be particularly interesting if you
plan to contribute in that area.</p>
<p>One of the goals of the PyPy project is to provide a fast and compliant
python interpreter. Some of the ways we achieve this are by providing a
high-performance garbage collector (GC) and a high-performance
......@@ -116,12 +324,10 @@
<tt class="docutils literal">reduce()</tt>, and to some extend <tt class="docutils literal">map()</tt> (although the simple case
is JITted), and to all usages of the <tt class="docutils literal">operator</tt> module we can think
of.</li>
<li><strong>Ctypes</strong>: Ctypes is a mixed bunch. If you're lucky you'll hit the
sweetspot and be <strong>really</strong> fast. If you're unlucky, you'll miss the
sweetspot and hit the slowpath which is much slower than CPython (2-10x
has been reported).</li>
<li><strong>Ctypes</strong>: Ctypes is slower than on CPython. Consider <a class="reference external" href="http://cffi.readthedocs.org/">CFFI</a> instead,
which has special paths inside the JIT.</li>
</ul>
<p>We generally consider things that are slower on PyPy than CPython to be bugs
of PyPy. If you find some issue that is not documented here,
please report it to our <a class="reference external" href="http://bugs.pypy.org">bug tracker</a> for investigation.</p>
</div>
......@@ -123,8 +329,9 @@
</ul>
<p>We generally consider things that are slower on PyPy than CPython to be bugs
of PyPy. If you find some issue that is not documented here,
please report it to our <a class="reference external" href="http://bugs.pypy.org">bug tracker</a> for investigation.</p>
</div>
</div>
<div id="sidebar">
</div>
</div>
......
......@@ -3,6 +3,252 @@
title: Performance
---
.. contents::
:depth: 1
This document collects strategies, tactics and tricks for making your
code run faster under PyPy. Many of these are also useful hints for
stock Python and other languages. For contrast, we also describe some
CPython (stock Python) optimizations that are not needed in PyPy.
Optimization strategy
=====================
These suggestions apply to all computer languages. They're here as
reminders of things to try before any Python or PyPy-specific tweaking.
Build a regression-test suite
-----------------------------
Before you start tuning, build a regression-test suite for your code.
This front-loads a significant amount of work, but it means you can
try lots of optimizations without worrying so much about introducing
functional bugs.
Measure, don't guess
--------------------
Human beings are bad at guessing or intuiting where the hotspots in code are.
Measure, don't guess; use a profiler to pin down the 20% of the
code where the code is spending 80% of its time, then speed-tune that.
Measuring will save you a lot of effort wasted on tuning parts of the code
that aren't actually bottlenecks.
As you tune, re-profile frequently so you can see how the hottest spots
are shifting around.
I/O-bound is different from compute-bound
-----------------------------------------
Be aware of the difference between code that is compute-bound (slow
because it's doing a huge number of instructions) and code that is I/O
bound (slow because of disk or network delays).
Expect to get most of your gains from optimizing compute-bound code.
It's usually (though not always) a sign that you're near the end of
worthwhile tuning when profiling shows that the bulk of the
application's time is spent on network and disk I/O.
Tune your algorithms first
--------------------------
Generally, when your code is doing things that are O(n**2) or larger
in the size of your data set, the cost of those operations is going
to swamp any small gains you can pick up with the tricks we describe
here.
Tune your algorithms first. It's time to think about applying our
list of micro-tuning tips *after* you think you've optimized out
intrinsically expensive operations.
That said, be prepared for the possibility that you will discover
better-hidden algorithmic problems as you micro-tune. Likely
you will go through this cycle more than once.
Focus on tight loops
--------------------
It's extremely common for high time costs to lurk within some
innocuous-looking code inside a tight loop - especially in code
that does something like a searching/matching/lookup operation
or any kind of graph traversal.
Probably the most common kind of performance-killer in compute-bound
code is an O(n**2) operation that is disguised by being some sort of
O(n) lookup or match inside an O(n) loop.
Another common time-sink is relatively expensive common-setup
operations that are performed inside tight loops but could be moved
to before they start. (For a representative case of this, see the
micro-tuning tip on regexp compilation.)
Smaller is faster
-----------------
Modern computers have multiple levels of memory caching, some directly
on the processor chip. Causing a cache miss at any level incurs a
performance penalty proportional to random-access time for the next
outward (and much slower) layer of cache.
Accordingly, smaller is faster. Programs or routines with a small
enough working set to fit inside a fast cache will be as fast as
that cache is. To make your code fast, reduce the length of the
series of Python or JIT-compiler opcodes it generates by making
it simpler.
The tradeoff here is that algorithmic tuning often trades time for
space - that is, it increases the size of an algorithm's working set
by including pre-computations or tables or reverse maps in order to
avoid O(n**2) operations.
It's impossible to predict in advance where the sweet spot in that
tradeoff will be. You have to try different things and measure -
which takes us right back to "Measure, don't guess". And another
function of your regression test suite can be as a speed benchmark.
Micro-tuning tips
=================
These are in no particular order.
Keep it simple
--------------
Simple is better than complex. The PyPy JIT is not very smart; the
simpler your code is the better it will run. Here again, though, you face
a tradeoff: you may need to pay with more algorithmic complexity in order
to avoid brute-force operations that are O(n**2) or worse.
Write plain-vanilla code in plain-vanilla ways. The PyPy JIT has many
productions that optimize a common usage pattern against an uncommon
usage pattern.
Global variables
----------------
In CPython, global variables and functions (including package imports)
are much more expensive to reference than locals; avoid them. (This
is also good modularity practice).
The cost of CPython global references is high enough that, for example, if you
have code in a frequently-visited inner loop that uses int() a lot, it
may be worthwhile to create a local copy of the reference with "int =
int" in an enclosing block.
However, this in *not* true in JITted PyPy code. The "int = int" hack
won't buy you performance, it's just an extra copy. The modularity
reason for avoiding globals are still valid.
Regular expressions
-------------------
Regular-expression compilation is expensive. If the regexp pattern in
a search, match, or replace operation is static (doesn't mutate at
runtime) refactor so it's only done once.
If the regexp compilation is in a class method, consider doing it as
the initializer of a regexp-valued static (shared) class member and
using that class member in your operation.
If the regexp compilation is in a free function, consider moving it
to module level and referencing the resulting regexp object
(but see the warning above about global variables).
Old- vs. new-style classes
--------------------------
New-style classes allow faster attribute access and take up less core
per instance than old-style classes. Much of this advantage may be
lostm, however, if attribute names are not constant. For example: x.a
= y or even setattr(x, 'a', y) will be much faster than a dynamic
version: setattr(x, 'a' + some_variable, y).
Classes that inherit from both new- and old-style classes are
*extremely* slow; avoid at all costs.
In PyPy, isinstance() called against an old-style class was very slow
until 2.0.
String concatenation is expensive
----------------------------------
In CPython, you may want to replace::
s = head + body + maybe + tail
with the admittedly less readable::
s = "%(head)%(body)%(maybe)%(tail)" % locals()
or even::
s = "{head}{body}{maybe}{tail}".format(locals())
Both of the latter forms avoid multiple-allocation overhead.
But PyPy's JIT makes the overhead of intermediate concatenations
go away in linear code that keeps the number of concatenations
small, bound and constant. (And ``locals()`` is rather slow
with PyPy's JIT.)
On the other hand, in code like this with a string-valued foo() function::
for x in mylist:
s += foo(x)
the JIT cannot optimize out intermediate copies. This code is
actually quadratic in the total size of the mylist strings due to
repeated string copies of ever-larger prefix segments.
This::
parts = []
for x in mylist:
parts.append(foo(x))
s = "".join(parts)
can be much faster because all the string concatenation in the last
line creates exactly one new string object with one C-level copy
sequence (and list operations are relatively cheap).
Frame introspection and tracing are slow
----------------------------------------
Certain function calls can disable PyPy's speed options over
stretches of surrounding code called "JIT scopes".
A JIT like PyPy's works based on the assumption that the only thing
worth optimizing are loops that are executed often. Whenever the
interpreter enters a loop in the interpreted program, the JIT records
what the interpreter does, creating a trace. This trace is optimized,
compiled to machine code and executed when the loop is hit with the
conditions observed during tracing. This trace is one kind of JIT scope.
Another kind of JIT scope that matters is a function, considered as
a unit for inlining.
Note that a JIT scope is a run-time phenomenon, not a compile-time
one. It's not confined by source-code module boundaries. A library-
or foreign-module call in a frequently-called loop or inlined function
will be part of its JIT scope.
locals(), globals(), sys._getframe(), sys.exc_info(), and sys.settrace
work in PyPy, but they incur a performance penalty that can be huge by
disabling the JIT over the enclosing JIT scope.
One unobvious case where frame introspection is used is the logging
module. Don't use the logging module if you need to run fast.
*(Thanks Eric S. Raymond for the text above)*
Insider's point of view
=======================
This section describes performance issues from the point of view of
insiders of the project; it should be particularly interesting if you
plan to contribute in that area.
One of the goals of the PyPy project is to provide a fast and compliant
python interpreter. Some of the ways we achieve this are by providing a
high-performance garbage collector (GC) and a high-performance
......@@ -81,10 +327,8 @@
is JITted), and to all usages of the ``operator`` module we can think
of.
* **Ctypes**: Ctypes is a mixed bunch. If you're lucky you'll hit the
sweetspot and be **really** fast. If you're unlucky, you'll miss the
sweetspot and hit the slowpath which is much slower than CPython (2-10x
has been reported).
* **Ctypes**: Ctypes is slower than on CPython. Consider CFFI_ instead,
which has special paths inside the JIT.
We generally consider things that are slower on PyPy than CPython to be bugs
of PyPy. If you find some issue that is not documented here,
......@@ -92,4 +336,4 @@
.. _`bug tracker`: http://bugs.pypy.org
.. _`speed website`: http://speed.pypy.org
.. _CFFI: http://cffi.readthedocs.org/
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