Standard Interpreter Optimizations
- Object Optimizations
- Interpreter Optimizations
- Overall Effects
One of the advantages -- indeed, one of the motivating goals -- of the PyPy standard interpreter (compared to CPython) is that of increased flexibility and configurability.
One example of this is that we can provide several implementations of the same object (e.g. lists) without exposing any difference to application-level code. This makes it easy to provide a specialized implementation of a type that is optimized for a certain situation without disturbing the implementation for the regular case.
This document describes several such optimizations. Most of them are not enabled by default. Also, for many of these optimizations it is not clear whether they are worth it in practice for a real-world application (they sure make some microbenchmarks a lot faster and use less memory, which is not saying too much). If you have any observation in that direction, please let us know! By the way: alternative object implementations are a great way to get into PyPy development since you have to know only a rather small part of PyPy to do them. And they are fun too!
Similar to CPython, it is possible to enable caching of small integer objects to not have to allocate all the time when doing simple arithmetic. Every time a new integer object is created it is checked whether the integer is small enough to be retrieved from the cache.
This option is disabled by default, you can enable this feature with the :config:`objspace.std.withprebuiltint` option.
An even more aggressive way to save memory when using integers is "small int" integer implementation. It is another integer implementation used for integers that only needs 31 bits (or 63 bits on a 64 bit machine). These integers are represented as tagged pointers by setting their lowest bits to distinguish them from normal pointers. This completely avoids the boxing step, saving time and memory.
You can enable this feature with the :config:`objspace.std.withsmallint` option.
Multi-dicts are a special implementation of dictionaries. It became clear that it is very useful to change the internal representation of an object during its lifetime. Multi-dicts are a general way to do that for dictionaries: they provide generic support for the switching of internal representations for dicts.
If you just enable multi-dicts, special representations for empty dictionaries, for string-keyed dictionaries. In addition there are more specialized dictionary implementations for various purposes (see below).
This is now the default implementation of dictionaries in the Python interpreter.
Sharing dictionaries are a special representation used together with multidicts. This dict representation is used only for instance dictionaries and tries to make instance dictionaries use less memory (in fact, in the ideal case the memory behaviour should be mostly like that of using __slots__).
The idea is the following: Most instances of the same class have very similar attributes, and are even adding these keys to the dictionary in the same order while __init__() is being executed. That means that all the dictionaries of these instances look very similar: they have the same set of keys with different values per instance. What sharing dicts do is store these common keys into a common structure object and thus save the space in the individual instance dicts: the representation of the instance dict contains only a list of values.
A more advanced version of sharing dicts, called map dicts, is available with the :config:`objspace.std.withmapdict` option.
Range-lists solve the same problem that the xrange builtin solves poorly: the problem that range allocates memory even if the resulting list is only ever used for iterating over it. Range lists are a different implementation for lists. They are created only as a result of a call to range. As long as the resulting list is used without being mutated, the list stores only the start, stop and step of the range. Only when somebody mutates the list the actual list is created. This gives the memory and speed behaviour of xrange and the generality of use of range, and makes xrange essentially useless.
You can enable this feature with the :config:`objspace.std.withrangelist` option.
A method cache is introduced where the result of a method lookup is stored (which involves potentially many lookups in the base classes of a class). Entries in the method cache are stored using a hash computed from the name being looked up, the call site (i.e. the bytecode object and the current program counter), and a special "version" of the type where the lookup happens (this version is incremented every time the type or one of its base classes is changed). On subsequent lookups the cached version can be used, as long as the instance did not shadow any of its classes attributes.
You can enable this feature with the :config:`objspace.std.withmethodcache` option.
An unusual feature of Python's version of object oriented programming is the concept of a "bound method". While the concept is clean and powerful, the allocation and initialization of the object is not without its performance cost. We have implemented a pair of bytecodes that alleviate this cost.
For a given method call obj.meth(x, y), the standard bytecode looks like this:
LOAD_GLOBAL obj # push 'obj' on the stack LOAD_ATTR meth # read the 'meth' attribute out of 'obj' LOAD_GLOBAL x # push 'x' on the stack LOAD_GLOBAL y # push 'y' on the stack CALL_FUNCTION 2 # call the 'obj.meth' object with arguments x, y
We improved this by keeping method lookup separated from method call, unlike some other approaches, but using the value stack as a cache instead of building a temporary object. We extended the bytecode compiler to (optionally) generate the following code for obj.meth(x):
LOAD_GLOBAL obj LOOKUP_METHOD meth LOAD_GLOBAL x LOAD_GLOBAL y CALL_METHOD 2
LOOKUP_METHOD contains exactly the same attribute lookup logic as LOAD_ATTR - thus fully preserving semantics - but pushes two values onto the stack instead of one. These two values are an "inlined" version of the bound method object: the im_func and im_self, i.e. respectively the underlying Python function object and a reference to obj. This is only possible when the attribute actually refers to a function object from the class; when this is not the case, LOOKUP_METHOD still pushes two values, but one (im_func) is simply the regular result that LOAD_ATTR would have returned, and the other (im_self) is a None placeholder.
After pushing the arguments, the layout of the stack in the above example is as follows (the stack grows upwards):
|y (2nd arg)|
|x (1st arg)|
|function object (im_func)|
The CALL_METHOD N bytecode emulates a bound method call by inspecting the im_self entry in the stack below the N arguments: if it is not None, then it is considered to be an additional first argument in the call to the im_func object from the stack.
You can enable this feature with the :config:`objspace.opcodes.CALL_METHOD` option.
The impact these various optimizations have on performance unsurprisingly depends on the program being run. Using the default multi-dict implementation that simply special cases string-keyed dictionaries is a clear win on all benchmarks, improving results by anything from 15-40 per cent.
Another optimization, or rather set of optimizations, that has a uniformly good effect are the two 'method optimizations', i.e. the method cache and the LOOKUP_METHOD and CALL_METHOD opcodes. On a heavily object-oriented benchmark (richards) they combine to give a speed-up of nearly 50%, and even on the extremely un-object-oriented pystone benchmark, the improvement is over 20%.
When building pypy, all generally useful optimizations are turned on by default unless you explicitly lower the translation optimization level with the --opt option.