And my less performant 2.7 GHz Intel Core i5 MBP with Apple accelerated
VecLib is way faster than naive netlib BLAS (I guess it's multi-threaded):
| a b |
a := LapackDGEMatrix randNormal: #(1000 1000).
b := LapackDGEMatrix randNormal: #(1000 1000).
[a * b] timeToRun.
45
| a b |
a :=
Le mar. 21 mai 2019 à 18:55, Nicolas Cellier <
nicolas.cellier.aka.n...@gmail.com> a écrit :
> I have updated Smallapack to version 1.6.1 so as to accelerate sum.
>
> | a b c |
> a := LapackSGEMatrix randNormal: #(1 1).
> b := a as: FloatArray.
> c := a asAbstractMatrix.
> {a sum. b sum. c
I have updated Smallapack to version 1.6.1 so as to accelerate sum.
| a b c |
a := LapackSGEMatrix randNormal: #(1 1).
b := a as: FloatArray.
c := a asAbstractMatrix.
{a sum. b sum. c sum.}.
{[a sum] bench. [b sum] bench. [c sum] bench.}.
'27,500 per second. 36.3 microseconds per run.'
There is a new Pharo build available!
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Not a problem. I greatly respective other peoples time and priorities
and their personal lives.
Just for the record I am using 64bit Pharo on a fast i7, 16gb ram,
laptop running Xubunut 18.04 64bit.
I do not remember any problems loading. And within the small amount of
experimenting that I
There is a new Pharo build available!
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Hi Serge,
this is good news, having tensor flow bindings is also a must!
I have this in Smallapack with pure CPU unaccelerated blas (no MKL, nor
ATLAS, just plain and dumb netlib code)
| a b |
a := LapackDGEMatrix randNormal: #(1000 1000).
b := LapackDGEMatrix randNormal: #(1000 1000).
[a * b]
Hi Jimmie,
I didn't take time yesterday to analyze your specific example because it
was quite late, but here are some remarks:
1) First, I recommend using 64bits Pharo, because number crunching and
Float operations will be faster (not FloatArray though).
2) it would be nice to use a profiler to
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