I never said it was impossible, just very hard.
Le 17/01/2017 à 16:48, Stephan Houben a écrit :
> Hi Xavier,
>
> In this bright age of IEEE-754 compatible CPUs,
> it is certainly possible to achieve reproducible FP.
> I worked for a company whose software produced bit-identical
> results on vario
Makes sense, thanks! math.fma() it is. :)
On Tue, Jan 17, 2017, 7:48 AM Stephan Houben wrote:
> Hi Xavier,
>
> In this bright age of IEEE-754 compatible CPUs,
> it is certainly possible to achieve reproducible FP.
> I worked for a company whose software produced bit-identical
> results on vario
Hi Xavier,
In this bright age of IEEE-754 compatible CPUs,
it is certainly possible to achieve reproducible FP.
I worked for a company whose software produced bit-identical
results on various CPUs (x86, Sparc, Itanium) and OSes (Linux, Solaris,
Windows).
The trick is to closely RTFM for your CPU
> Generally speaking, there are two reasons why people may *not* want an
> FMA operation.
> 1. They need their results to be reproducible across
> compilers/platforms. (the most common reason)
>
The reproducibility of floating point calculation is very hard to reach
a good survey of the problem i
Hi Gregory,
2017-01-16 20:28 GMT+01:00 Gregory P. Smith :
> Is there a good reason not to detect single expression multiply adds and
> just emit a new FMA bytecode?
>
Yes ;-) See below.
> Is our goal for floats to strictly match the result of the same operations
> coded in unoptimized C using d
On 16.01.2017 20:28, Gregory P. Smith wrote:
Is there a good reason not to detect single expression multiply adds
and just emit a new FMA bytecode?
Same question here.
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Is there a good reason not to detect single expression multiply adds and
just emit a new FMA bytecode?
Is our goal for floats to strictly match the result of the same operations
coded in unoptimized C using doubles?
Or can we be more precise on occasion?
I guess a similar question may be asked o
My understanding is that NumPy does NOT currently support a direct FMA
operation "natively." However, higher-level routines like
`numpy.linalg.solve` that are linked to MKL or BLAS DO take advantage of
FMA within the underlying libraries.
On Mon, Jan 16, 2017 at 10:06 AM, Guido van Rossum
wrote:
Does numpy support this?
--Guido (mobile)
On Jan 16, 2017 7:27 AM, "Stephan Houben" wrote:
> Hi Steve,
>
> Very good!
> Here is a version which also handles the nan's, infinities,
> negative zeros properly.
>
> ===
> import math
> from fractions import Fraction
>
> def fma2(x, y, z)
Hi Steve,
Very good!
Here is a version which also handles the nan's, infinities,
negative zeros properly.
===
import math
from fractions import Fraction
def fma2(x, y, z):
if math.isfinite(x) and math.isfinite(y) and math.isfinite(z):
result = float(Fraction(x)*Fraction(y
On Mon, Jan 16, 2017 at 11:01:23AM +0100, Stephan Houben wrote:
[...]
> So the following would not be a valid FMA fallback
>
> double bad_fma(double x, double y, double z) {
> return x*y + z;
> }
[...]
> Upshot: if we want to provide a software fallback in the Python code, we
> need to do somet
Hi Victor,
The fallback implementations in the various libc take care
to preserve the correct rounding behaviour.
Let me stress that *fused* multiply-add means the specific rounding
behaviour as defined in the standard IEEE-754 2008
(i.e. with guaranteed *no* intermediate rounding).
So the follo
2017-01-15 18:25 GMT+01:00 Juraj Sukop :
> C99 includes `fma` function to `math.h` [6] and emulates the calculation if
> the FMA instruction is not present on the host CPU [7].
If even the libc function has a fallback on x*y followed by +z, it's
fine to add such function to the Python stdlib. It m
On Sun, Jan 15, 2017 at 5:25 PM, Juraj Sukop wrote:
> This proposal is then about adding new `fma` function with the following
> signature to `math` module:
>
> math.fma(x, y, z)
Sounds good to me. Please could you open an issue on the bug tracker
(http://bugs.python.org)?
Thanks,
Mark
On Mon, Jan 16, 2017 at 4:25 AM, Juraj Sukop wrote:
> There is a simple module for Python 3 demonstrating the fused multiply-add
> operation which was build with simple `python3 setup.py build` under Linux
> [9].
>
> Any feedback is greatly appreciated!
+1. Just tried it out, and apart from dropp
Hi Juraj,
I think this would be a very useful addition to the `math' module.
The gating issue is probably C compiler support.
The most important non-C99 C compiler for Python is probably MS Visual
Studio.
And that one appears to support it:
https://msdn.microsoft.com/en-us/library/mt720715.aspx
Hello!
Fused multiply-add (henceforth FMA) is an operation which calculates the
product of two numbers and then the sum of the product and a third number
with just one floating-point rounding. More concretely:
r = x*y + z
The value of `r` is the same as if the RHS was calculated with infinit
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