Performance

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.

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 lost, 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)s%(body)s%(maybe)s%(tail)s" % 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. (Such code is always fine for bytearrays, because in this case += is an in-place operation.)

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.

(Thanks Eric S. Raymond for the text above)