Yes I'm very familiar with BLIS and my own BLAS is implemented following the
BLIS paper
[http://www.cs.utexas.edu/users/flame/pubs/blis3_ipdps14.pdf](http://www.cs.utexas.edu/users/flame/pubs/blis3_ipdps14.pdf)
Actually some BLIS developers are also aware of my own efforts:
*
[https://github
I have randomly wondered onto
[https://github.com/flame/blis#key-features](https://github.com/flame/blis#key-features)
today and haven't had a chance to play with it, but it sure looks interesting;
Are you familiar with it? Obviously less relevant if you're going to do a pure
Nim BLAS ...
I didn't even know it was in Kostya, but technically just like Julia, Numpy,
and Lubeck (D) the current version of Arraymancer is just using BLAS behind
(which are 95% C)
That said I do get similar result in a pure Nim BLAS, with both OpenMP-based or
with Weave-based threading:
* code:
[htt
Very impressive matmul results in [Kostya's
benchmarks](https://github.com/kostya/benchmarks).
Would be even better if s/Apache/MIT/ license...
I've released version 0.6 of Arraymancer (January 2020).
[https://github.com/mratsim/Arraymancer/releases/tag/v0.6.0](https://github.com/mratsim/Arraymancer/releases/tag/v0.6.0)
Well I said that 0.6 will be the switch to
[Laser](https://github.com/numforge/laser), it didn't happen because my tim
I've released a new stable version of Arraymancer v0.5.1.
The number of changes is small in number but great in quality:
[https://github.com/mratsim/Arraymancer/releases/tag/v0.5.1](https://github.com/mratsim/Arraymancer/releases/tag/v0.5.1)
Changes affecting backward compatibility:
* None
**Intro to Tensors**
[https://github.com/juancarlospaco/nim-presentation-slides/tree/master/ejemplos/avanzado/tensorflow#intro-to-tensors](https://github.com/juancarlospaco/nim-presentation-slides/tree/master/ejemplos/avanzado/tensorflow#intro-to-tensors)
There is a [memowner bool in the
type](https://github.com/numforge/laser/blob/990e59fffe50779cdef33aa0b8f22da19e1eb328/laser/tensor/datatypes.nim#L12-L30).
If it's false, it will not deallocate memory when not referenced.
const LASER_MAXRANK*{.intdefine.} = 6
type DynamicSt
@mratsim: If everything is a (pointer, len) pair, who do you deal with the
ownership problems?
Something I read about today, another approach to implementing tensors...
[http://nlp.seas.harvard.edu/NamedTensor](http://nlp.seas.harvard.edu/NamedTensor)
Awesome work!, cool Xmas gift!. ;P
Great!!!
Seems like I can't change title anymore with the new forum :/.
Arraymancer is now at version 0.5: [release
announcement.](https://github.com/mratsim/Arraymancer/releases/tag/v0.5.0)
Here are the highlights:
* Backward incompatible: PCA now returns a tuple of the projected tensor and
the
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