On Thu, May 15, 2025 at 03:41:52PM +0200, PIERRE AUGIER via NumPy-Discussion
wrote:
> ========== PyPy HPy univ / CPy native (time ratio, smaller is better)
> ==========
> TestModule::test_noargs 0.50
> TestModule::test_onearg_None 0.60
> TestModule::test_onearg_int 0.65
> TestModule::test_varargs 0.93
> TestModule::test_call_with_tuple 2.21
> TestModule::test_call_with_tuple_and_dict 1.50
> TestModule::test_allocate_int 0.67
> TestModule::test_allocate_tuple 6.33
> TestType::test_allocate_obj 5.89
> TestType::test_method_lookup 0.05
> TestType::test_noargs 0.85
> TestType::test_onearg_None 0.95
> TestType::test_onearg_int 0.97
> TestType::test_varargs 1.18
> TestType::test_len 0.48
> TestType::test_getitem 0.73
> TestHeapType::test_allocate_obj_and_survive 5.11
> TestHeapType::test_allocate_obj_and_die 4.07
> TestHeapType::test_method_lookup 0.05
> TestHeapType::test_noargs 0.84
> TestHeapType::test_onearg_None 0.93
> TestHeapType::test_onearg_int 0.96
> TestHeapType::test_varargs 1.20
> TestHeapType::test_len 0.50
> TestHeapType::test_getitem 0.78
Thanks, that does look encouraging except for object creation times. I think hpy
could benefit from smaller benchmark examples. For example, I didn't find one
with PyNumberMethods except in numpy-hpy, which does some numpy-specific dark
magic.
This is the benchmark I tried to achieve but gave up after 30 min:
===================================================================================
#
# Fake should be an immutable object that uses tp_new but not tp_init and
# has one nb_add number method. The method can cheat and just return a new
# Fake() without doing anything. The point of the benchmark is to test the
# speed of nb_add and object creation.
#
from fake import Fake
a = Fake()
b = Fake()
for _ in range(10000000):
x = a + b
y = b + x
a = x
b = y
print(y) # In case optimizing compilers have gotten smart and eliminate the
loop.
===================================================================================
Stefan Krah
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