Representation of 128 bit floating point numbers in FreeBSD amd64 and Clang
Mehmet Erol Sanliturk
m.e.sanliturk at gmail.com
Thu Oct 31 22:11:37 UTC 2013
On Thu, Oct 31, 2013 at 5:38 PM, Bruce Evans <brde at optusnet.com.au> wrote:
> On Thu, 31 Oct 2013, Steve Kargl wrote:
> On Thu, Oct 31, 2013 at 09:27:34AM -0400, Mehmet Erol Sanliturk wrote:
>>> In FreeBSD amd64 and Clang ,
>>> how can I represent 128 bits ( 34 digits ) variables ?
> With difficulty, since it is not supported.
> Not sure it can be done with clang, but GCC supports
>> a __float128 type. GCC refers to this as its TCmode.
>> gfortran, the Fortran compiler that supports REAL(16),
>> uses __float128 internally. I've never directly used
>> __float128, so can't help beyond this.
>> If you need 128-bits in C on ia32 or x86_64 hardware,
>> you should probably look into using mpfr and mpc.
> Even gcc-4.2.1 in FreeBSD generates code to use __float128,
> but the support for it isn't compiled into libgcc for some
> Why would anyone want to use 128-bit FP on x86? It is emulated
> similarly to on sparc64. On sparc64, emulated 128-bit FP is about
> 100 times slower than hardware 64-bit FP. The emulation is not
> very good, but 128-bit FP is part of the ABI on sparc64 so I would
> expect the emulation to give an even larger slowdown factor in
> With 80-bit FP, you can't quite exactly count the number of atoms in
> the universe, but you can count the world's GNP in cents for a thousand
> years or so. Extra accuracy can reduce problems from numerica
> instability and rounding bugs, but a slowdown factor of 100 times is
> a large price to pay for that.
For ill-conditioned problems and especially when the result is NOT known in
use of larger number of digits ( 64 bits versus 80 bits versus 128 bits
versus arbitrary precision )
is much more important from time consumed for the computations .
For example , a polynomial ( with degree 12 ) largest root as 63.xxxxxx|7 (
giving nearly zero for the polynomial ) and 63.xxxxxx|9 ( giving 10 ** 25 (
twenty five zeros at the right of 1 without period ) ) .
On such problems , difference of double precision and quadruple precision
is apparent .
Without arbitrary precision arithmetic , it is not possible to solve
problems after a small number of parameters .
As an example , it may be a very useful experience to invert Hilbert matrix
with single , double and quadruple precision arithmetic to see up to what
degree a correct inverse can be obtained .
My decision is to rewrite all of my numerical analysis programs from
scratch by using arbitrary precision arithmetic because current ( double
precision ) computations are physically useless when the answer is not
known in advance such as sum of the squares should be zero , or a root
should give zero as polynomial value . Even for such cases , to find a
usable results are extremely difficult because when number of parameters
increases errors are dominating the results .
Some large number of parameter problem examples in numerical analysis books
or papers are very misleading because when a different initial value set is
given , the algorithms are collapsing immediately .
Therefore , number of digits in computations is much more important than
any other factor such as time .
Thank you very much .
Mehmet Erol Sanliturk
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