python 32-bit and 64-bit integer math with intentional overflow

Question

What's the best way to do integer math in 32- and 64-bit, so that overflow happens like it does in C?

e.g. (65536*65536+1)*(65536*65536+1) should be 0x0000000200000001 in 64-bit math, and not its exact value (non-overflowing) 0x10000000200000001.

Signed overflow causes undefined behaviour in C, so strictly speaking the question is meaningless. — Cairnarvon
– Cairnarvon, Commented May 25, 2013 at 1:22
^ yet another reason C isn't helpful for certain types of numerical computation. :-( — Jason S
– Jason S, Commented May 25, 2013 at 4:32
Similar, but not a duplicate. The 32-bit question (or 16-bit, which I didn't ask about) is different from the int -> long int behavior. — Jason S
– Jason S, Commented Jul 27, 2015 at 13:42

martineau · Accepted Answer · 2013-05-25 00:56:28Z

34

Just & the result with the appropriate 32- or 64-bit mask (0xffffffff or 0xffffffffffffffff).

answered May 25, 2013 at 0:56

martineau

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1 Comment

Gregory Morse Over a year ago

With signed int you have to use modulo e.g. % 0x100000000 or % 0x10000000000000000. Unfortunately this is not really efficient. Neither is the bitmask when compared to any language that supports native processor sized integers. NumPy exposes them so its probably the best solution still...

Jens · Accepted Answer · 2016-04-16 05:18:23Z

16

Use NumPy with the appropriate integer size and the overflow is more C like:

32 bit:

>>> np.uint32(2**32-3) + np.uint32(5)
__main__:1: RuntimeWarning: overflow encountered in uint_scalars
2

64 bit:

>>> i64=np.uint64(65536*65536+1)
>>> hex(i64*i64)
'0x200000001L'

Compare with Python's native int:

>>> hex((65536*65536+1)*(65536*65536+1))
'0x10000000200000001L'

You can see that NumPy is doing as you desire.

edited Apr 16, 2016 at 5:18

Jens

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answered May 25, 2013 at 0:57

dawg

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2 Comments

Gregory Morse Over a year ago

Can NumPy omit the warnings though? This is probably the best solution as it would be the fastest and deals with signed/unsigned rather than hacking it with bit masks or modulo over in the Python native bigint side of things.

dawg Over a year ago

Yes, you can catch or suppress the errors and warnings. You can do that with the warnings module or with multiple numpy options.

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