What is the internal precision of numpy.float128?
Question:
What precision does numpy.float128 map to internally? Is it __float128 or long double? Or something else entirely?
A potential follow on question if anybody knows: is it safe in C to cast a __float128 to a (16 byte) long double, with just a loss in precision? (this is for interfacing with a C lib that operates on long doubles).
Edit: In response to the comment, the platform is ‘Linux-3.0.0-14-generic-x86_64-with-Ubuntu-11.10-oneiric’. Now, if numpy.float128 has varying precision dependent on the platform, that is also useful knowledge for me!
Just to be clear, it is the precision I am interested in, not the size of an element.
Answers:
It’s quite recommended to use longdouble instead of float128, since it’s quite a mess, ATM. Python will cast it to float64 during initialization.
Inside numpy, it can be a double or a long double. It’s defined in npy_common.h and depends of your platform. I don’t know if you can include it out-of-the-box into your source code.
If you don’t need performance in this part of your algorithm, a safer way could be to export it to a string and use strold afterwards.
numpy.longdouble refers to whatever type your C compiler calls long double. Currently, this is the only extended precision floating point type that numpy supports.
On x86-32 and x86-64, this is an 80-bit floating point type. On more exotic systems it may be something else (IIRC on Sparc it’s an actual 128-bit IEEE float, and on PPC it’s double-double). (It also may depend on what OS and compiler you’re using — e.g. MSVC on Windows doesn’t support any kind of extended precision at all.)
Numpy will also export some name like numpy.float96 or numpy.float128. Which of these names is exported depends on your platform/compiler, but whatever you get always refers to the same underlying type as longdouble. Also, these names are highly misleading. They do not indicate a 96- or 128-bit IEEE floating point format. Instead, they indicate the number of bits of alignment used by the underlying long double type. So e.g. on x86-32, long double is 80 bits, but gets padded up to 96 bits to maintain 32-bit alignment, and numpy calls this float96. On x86-64, long double is again the identical 80 bit type, but now it gets padded up to 128 bits to maintain 64-bit alignment, and numpy calls this float128. There’s no extra precision, just extra padding.
Recommendation: ignore the float96/float128 names, just use numpy.longdouble. Or better yet stick to doubles unless you have a truly compelling reason. They’ll be faster, more portable, etc.
TLDR from the numpy docs:
np.longdouble is padded to the system default; np.float96 and np.float128 are provided for users who want specific padding. In spite of the names, np.float96 and np.float128 provide only as much precision as np.longdouble, that is, 80 bits on most x86 machines and 64 bits in standard Windows builds.
What precision does numpy.float128 map to internally? Is it __float128 or long double? Or something else entirely?
A potential follow on question if anybody knows: is it safe in C to cast a __float128 to a (16 byte) long double, with just a loss in precision? (this is for interfacing with a C lib that operates on long doubles).
Edit: In response to the comment, the platform is ‘Linux-3.0.0-14-generic-x86_64-with-Ubuntu-11.10-oneiric’. Now, if numpy.float128 has varying precision dependent on the platform, that is also useful knowledge for me!
Just to be clear, it is the precision I am interested in, not the size of an element.
It’s quite recommended to use longdouble instead of float128, since it’s quite a mess, ATM. Python will cast it to float64 during initialization.
Inside numpy, it can be a double or a long double. It’s defined in npy_common.h and depends of your platform. I don’t know if you can include it out-of-the-box into your source code.
If you don’t need performance in this part of your algorithm, a safer way could be to export it to a string and use strold afterwards.
numpy.longdouble refers to whatever type your C compiler calls long double. Currently, this is the only extended precision floating point type that numpy supports.
On x86-32 and x86-64, this is an 80-bit floating point type. On more exotic systems it may be something else (IIRC on Sparc it’s an actual 128-bit IEEE float, and on PPC it’s double-double). (It also may depend on what OS and compiler you’re using — e.g. MSVC on Windows doesn’t support any kind of extended precision at all.)
Numpy will also export some name like numpy.float96 or numpy.float128. Which of these names is exported depends on your platform/compiler, but whatever you get always refers to the same underlying type as longdouble. Also, these names are highly misleading. They do not indicate a 96- or 128-bit IEEE floating point format. Instead, they indicate the number of bits of alignment used by the underlying long double type. So e.g. on x86-32, long double is 80 bits, but gets padded up to 96 bits to maintain 32-bit alignment, and numpy calls this float96. On x86-64, long double is again the identical 80 bit type, but now it gets padded up to 128 bits to maintain 64-bit alignment, and numpy calls this float128. There’s no extra precision, just extra padding.
Recommendation: ignore the float96/float128 names, just use numpy.longdouble. Or better yet stick to doubles unless you have a truly compelling reason. They’ll be faster, more portable, etc.
TLDR from the numpy docs:
np.longdoubleis padded to the system default;np.float96andnp.float128are provided for users who want specific padding. In spite of the names,np.float96andnp.float128provide only as much precision asnp.longdouble, that is, 80 bits on most x86 machines and 64 bits in standard Windows builds.