#11900: Serious regression caused by #9138
------------------------------+---------------------------------------------
Reporter: SimonKing | Owner: tbd
Type: defect | Status: needs_work
Priority: critical | Milestone: sage-4.8
Component: performance | Keywords: categories regression
Work_issues: | Upstream: N/A
Reviewer: Jeroen Demeyer | Author: Simon King
Merged: | Dependencies: #9138 #11911
------------------------------+---------------------------------------------
Comment(by SimonKing):
I changed `Rings(Category)` into `Rings(Category_singleton)` (but then had
to remove the `rings_contains` function in the pyx file, by cyclic
import).
First, because of your optimisation of the classcall, one has
{{{
sage: %timeit Rings()
625 loops, best of 3: 3.42 µs per loop
}}}
Second, one has
{{{
sage: R = Rings()
sage: P.<x,y,z> = QQ[]
sage: P in R
True
sage: %timeit P in R
625 loops, best of 3: 1.38 µs per loop
}}}
Using `PyType_IsSubtype` from sage/ext/python_type.pxi, one comes down to
1.08 µs.
You use a lazy class attribute not only for the classcall, but also for
the containment test. If one writes the containment test directly as a
method (recall: We are here in a cython file!), one has 1.53 µs. So, to my
surprise, the lazy class attribute trick works well also for performance!
Using a similar approach for computing the hash works as well. Namely, for
a singleton class, it should be fine if the hash of the (single) instance
is not id of the instance but id of the class. With the following code
{{{
#!python
cdef class hash_c(object):
cdef long n
def __init__(self, cls):
self.n = id(cls)
def __call__(self):
return self.n
class Category_singleton(Category):
...
@lazy_class_attribute
def __hash__(cls):
return hash_c(cls)
}}}
I obtain
{{{
sage: %timeit {R:1}
625 loops, best of 3: 695 ns per loop
}}}
Without that trick, it is 1.46 µs.
--
Ticket URL: <http://trac.sagemath.org/sage_trac/ticket/11900#comment:107>
Sage <http://www.sagemath.org>
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