[SciPy-user] Python on Intel Xeon Dual Core Machine
Tue Feb 5 14:47:28 CST 2008
J. Ryan Earl wrote:
> Lorenzo Isella wrote:
>> I am a bit surprised at the fact that postprocessing some
>> relatively large arrays of data (5000 by 5000) takes a lot of time and
>> memory on my laptop, but the situation does not improve dramatically
>> on my desktop, which has more memory and is a 64-bit machine (with the
>> amd64 Debian).
>> A question: if I use arrays in Scipy without any special declaration,
>> are they double precision arrays or something "more" as a default on
>> 64-bit machines?
> I see a lot of confusion on this topic in general. When people talk
> about a "64-bit" machine in general CPU terms, they're talking about its
> address space. You're mixing up the size of address operands with the
> size of data operands.
He's not really confusing the two. Many systems change the size of the data
operands based on the size of the address operands.
As a general rule, though, only C integer types change size; the C standard is
notoriously flexible in this regard. This has some downstream effects: Python's
int object are stored with C longs and numpy's default "int" dtype is whatever
size that is.
While a system could theoretically change its default floating point type based
on the 64-bitness of the CPU/compiler combination, I've never seen anything do that.
"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth."
-- Umberto Eco
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