Hi all↵
↵
Firstly thanks for reading this. It's kinda hard to articulate what I'm doing and I hope that I've written it clearly.↵
↵
I tried playing around with long (larger than 1<<31) and ints in PyPy3 to see how performance is affected. If I'm not mistaken, numbers larger than 1<<31, or numbers originating from such numbers (eg. divided from a large number) are stored as longs in the underlying C struct. Smaller numbers are stored as int32.↵
↵
For a control experiment, I tried storing large numbers:↵
↵
//Just storing Longs as control experiment.↵
Source:↵
↵
~~~~~↵
x=1<<59↵
↵
y=[]↵
for _ in range(10**7):↵
y.append(x)↵
↵
~~~~~↵
↵
Output:↵
↵
=====↵
Used: 873 ms, 113092 KB↵
______↵
↵
I then proceeded to try converting the Long to int. Note that I did a division initially (x>>1). Thisiwas intentional, as it slowed down the code significantly. The code would run much faster (~300ms) without this step. Note how the memory used has reduced as (I believe) the numbers are now stored as int32.↵
↵
//Long to int directly, with dividing long by 2.↵
Source:↵
↵
~~~~~↵
x=1<<59↵
x=x>>1↵
↵
print(id(x))↵
↵
y=[]↵
for _ in range(10**7):↵
y.append(int(1.0*(x>>31)))↵
~~~~~↵
↵
Output:↵
4611686018427387905↵
↵
=====↵
Used: 1669 ms, 76632 KB↵
↵
______↵
↵
Finally, I attempted the same, but I assigned x to temp, deleted x, and reassigned temp to x. The code ran much faster (~300ms). But I have no idea why. I printed out the ids of x before and after to verify that the object (and hence its properties) didn't change.↵
↵
//Long to int, with dividing long by 2. But deleting the long and reassigning the long.↵
Source:↵
↵
~~~~~↵
x=1<<59↵
x=x>>1↵
↵
print(id(x))↵
temp=x↵
del x↵
x=temp↵
print(id(x)) #id does not change. i.e. same object, so properties shouldn't change↵
↵
y=[]↵
for _ in range(10**7):↵
y.append(int(1.0*(x>>31)))↵
↵
~~~~~↵
↵
↵
Output:↵
4611686018427387905↵
4611686018427387905↵
↵
=====↵
Used: 358 ms, 76796 KB↵
↵
______↵
Any idea what is happening here?
↵
Firstly thanks for reading this. It's kinda hard to articulate what I'm doing and I hope that I've written it clearly.↵
↵
I tried playing around with long (larger than 1<<31) and ints in PyPy3 to see how performance is affected. If I'm not mistaken, numbers larger than 1<<31, or numbers originating from such numbers (eg. divided from a large number) are stored as longs in the underlying C struct. Smaller numbers are stored as int32.↵
↵
For a control experiment, I tried storing large numbers:↵
↵
//Just storing Longs as control experiment.↵
Source:↵
↵
~~~~~↵
x=1<<59↵
↵
y=[]↵
for _ in range(10**7):↵
y.append(x)↵
↵
~~~~~↵
↵
Output:↵
↵
=====↵
Used: 873 ms, 113092 KB↵
______↵
↵
I then proceeded to try converting the Long to int. Note that I did a division initially (x>>1). This
↵
//Long to int directly, with dividing long by 2.↵
Source:↵
↵
~~~~~↵
x=1<<59↵
x=x>>1↵
↵
print(id(x))↵
↵
y=[]↵
for _ in range(10**7):↵
y.append(int(1.0*(x>>31)))↵
~~~~~↵
↵
Output:↵
4611686018427387905↵
↵
=====↵
Used: 1669 ms, 76632 KB↵
↵
______↵
↵
Finally, I attempted the same, but I assigned x to temp, deleted x, and reassigned temp to x. The code ran much faster (~300ms). But I have no idea why. I printed out the ids of x before and after to verify that the object (and hence its properties) didn't change.↵
↵
//Long to int, with dividing long by 2. But deleting the long and reassigning the long.↵
Source:↵
↵
~~~~~↵
x=1<<59↵
x=x>>1↵
↵
print(id(x))↵
temp=x↵
del x↵
x=temp↵
print(id(x)) #id does not change. i.e. same object, so properties shouldn't change↵
↵
y=[]↵
for _ in range(10**7):↵
y.append(int(1.0*(x>>31)))↵
↵
~~~~~↵
↵
↵
Output:↵
4611686018427387905↵
4611686018427387905↵
↵
=====↵
Used: 358 ms, 76796 KB↵
↵
______↵
Any idea what is happening here?
