In the last week, I (Armin) have been taking some time off the
JIT work to improve our GCs. More precisely, our GCs now take
one or two words less for every object. This further reduce the
memory usage of PyPy, as we will show at the end.
Background information: RPython object model
We first need to understand the RPython object model as
implemented by our GCs and our C backend. (Note that the
object model of the Python interpreter is built on top of
that, but is more complicated -- e.g. Python-level objects
are much more flexible than RPython objects.)
Consider these two RPython classes:
class A:
def __init__(self, x):
self.x = x
def f(self):
return self.x * 42
class B(A):
def __init__(self, x, y):
self.x = x
self.y = y
def f(self):
return self.x + self.y
The instances of A and B look like this in memory (all cells
are one word):
| GC header |
vtable ptr of A |
hash |
x |
| GC header |
vtable ptr of B |
hash |
x |
y |
The first word, the GC header, describes the layout. It
encodes on half a word the shape of the object, including where it
contains further pointers, so that the GC can trace it. The
other half contains GC flags (e.g. the mark bit of a
mark-and-sweep GC).
The second word is used for method dispatch. It is similar to a
C++ vtable pointer. It points to static data that is mostly a
table of methods (as function pointers), containing e.g. the method f
of the example.
The hash field is not necessarily there; it is only present in classes
whose hash is ever taken in the RPython program (which includes being
keys in a dictionary). It is an "identity hash": it works like
object.__hash__() in Python, but it cannot just be the address of
the object in case of a GC that moves objects around.
Finally, the x and y fields are, obviously, used to store the value
of the fields. Note that instances of B can be used in places that
expect a pointer to an instance of A.
Unifying the vtable ptr with the GC header
The first idea of saving a word in every object is the observation
that both the vtable ptr and the GC header store information about
the class of the object. Therefore it is natural to try to only have
one of them. The problem is that we still need bits for the GC flags,
so the field that we have to remove is the vtable pointer.
This means that method dispatch needs to be more clever: it
cannot directly read the vtable ptr, but needs to compute it
from the half-word of the GC header. Fortunately, this can be
done with no extra instruction on the assembler level. Here is
how things will look like in the end, assuming a 32-bit x86
machine (but note that as usual we just generate portable C).
The trick for achieving efficiency is that we store all
vtables together in memory, and make sure that they don't take
more than 256 KB in total (16 bits, plus 2 bits of alignment).
Here is how the assembler code (produced by the normal C
compiler, e.g. gcc) for calling a method looks like. Before
the change:
MOV EDX, [EAX + 4] # load the vtable ptr from object EAX
MOV EDX, [EDX + method_offset] # load the function pointer from the vtable
CALL EDX
Instead, we now have:
MOVZX EDX, [EAX] # load the 16-bit part of the GC header from EAX
MOV EDX, [vtable_start + 4*EDX + method_offset]
CALL EDX
Note that the complex addressing scheme done by the second MOV
is still just one instruction: the vtable_start and
method_offset are constants, so they are combined. And as the
vtables are anyway aligned at a word boundary, we can use
4*EDX to address them, giving us 256 KB instead of just 64 KB
of vtables.
Optimizing the hash field
In PyPy's Python interpreter, all application-level objects
are represented as an instance of some subclass of W_Root.
Since all of these objects could potentially be stored in a
dictionary by the application Python program, all these
objects need a hash field. Of course, in practice, only a
fraction of all objects in a Python program end up having
their hash ever taken. Thus this field of W_Root is wasted
memory most of the time.
(Up to now, we had a hack in place to save the hash field
on a few classes like W_IntegerObject, but that meant that
the Python expression ``object.__hash__(42)'' would raise
a TypeError in PyPy.)
The solution we implemented now (done by some Java GCs, among
others) is to add a hash field to an object when the
(identity) hash of that object is actually taken. This means
that we had to enhance our GCs to support this. When objects
are allocated, we don't reserve any space for the hash:
object at 0x74B028
When the hash of an object is taken, we use its current memory
address, and set a flag in the GC header saying that this
particular object needs a hash:
object at 0x74B028
If the GC needs to move the object to another memory location,
it will make the new version of the object bigger, i.e. it
will also allocate space for the hash field:
object at 0x825F60
This hash field is immediately initialized with the old memory
address, which is the hash value that we gave so far for the
object. To not disturb the layout of the object, we always
put the extra hash field at the end. Of course, once set,
the hash value does not change even if the object needs to
move again.
Results
Running the following program on PyPy's Python interpreter
with n=4000000:
def make_linked_list(n):
a = None
i = 0
while i < n:
b = X()
b.next = a
a = b
i += 1
the two optimizations together save 32 MB of RAM (i.e. 8 bytes
per object). The version of PyPy we measured this with was built
as follows:
./translate.py --gcremovetypeptr targetpypystandalone --objspace-std-withsharingdict
The total amount of RAM used on a 32-bit Linux is 247 MB,
completing in 10.3 seconds. On CPython, it consumes 684 MB
and takes 89 seconds to complete... This nicely shows that
our GCs are much faster at allocating objects, and that our
objects can be much smaller than CPython's.
Armin Rigo & Carl Friedrich Bolz
