Merge refactored collenchyma code

[jonysy]

Useful

decoupling branch
Backend decoupling and typed memory interface #63
collenchyma-cuda-refactor
alexandermorozov/collenchyma
Refactor synchronization #62 PR
simplify syncing

Gitter channel:

alexander: @\matrixbot I think f32 should be enough. Nvidia has recently introduced support for f16 in Pascal GPUs, so f16 is probably enough too most of the time

alexander: @\jonysy collenchyma-blas supports some simple operations... For A * 3 there is Scal trait with scal() function on backend

jonysy: Hello @\alexandermorozov. I’m glad you joined. Would you mind if I merge your PR with my hard fork of Collenchyma?

alexander: @\jonysy Of course merge it, I'll be happy that the code is used. You can also cherry-pick from my repo, it may be more convinient. That said, most of my currently opened PRs add autosync to SharedTensor, and in this project I think it would be better to have explicit sync as graph node

jonysy: @\alexandermorozov Great!

jonysy: Unfortunately, there’s currently no support for BLAS on OpenCL using Rust 😕.
Is there an alternative approach I can take?

cathal: Actively learning Arrayfire right now with that goal in mind.

alexander: @\jonysy You can use collenchyma with "native" backend, but that's not fun ) There was a rust project that aimed to support NNs on openCL, and it afaik it had some blas functions implemented

alexander: @\jonysy deeplearn-rs

jonysy: Hmm. I couldn’t locate any blas functions..
Looks like everything has been moved over to https://github.com/tedsta/gpuarray-rs
Thanks

cathal: I asked him about gpuarray-rs (see the issues pane) and he feels it's immature and needs a major refactor to be more composable/maintainable. IIRC, he was open to participation.