DIP80: phobos additions

[anonymous via Digitalmars-d]

>> 1. Create generalised (only type template and my be flags) 
>> BLAS algorithms (probably  slow) with CBLAS like API.
See [1] (the Matmul benchmark) Julia Native is probably backed 
with Intel MKL or OpenBLAS. D version was optimized by Martin 
Nowak [2] and is still _much_ slower.

>> 2. Allow users to use existing CBLAS libraries inside 
>> generalised BLAS.
I think a good interface is more important than speed of default 
implementation (at least for e.g large matrix multiplication). 
Just use existing code for speed...
Goto's papers about his BLAS: [3][4]
Having something a competitive in D would be great but probably a 
lot of work. Without a good D interface  dstep + openBLAS/Atlas 
header will not look that bad. Note I am not talking about small 
matrices/graphics.

>> 3. Start to improve generalised BLAS with SIMD instructions.
nice, but not really important. Good interface to existing high 
quality BLAS seems more important to me than fast D linear 
algebra implementation + CBLAS like interface.

>> 4. And then continue discussion about type of matrixes we 
>> want...
>
+1

> 2. Then write similar code with hardware optimized BLAS and 
> benchmark where the overhead between pure C/LLVM and BLAS calls 
> balance out to even.
may there are more important / beneficial things to work on - 
assuming total time of contributors is fix and used for other D 
stuff:)

[1] https://github.com/kostya/benchmarks
[2] https://github.com/kostya/benchmarks/pull/6
[3] http://www.cs.utexas.edu/users/flame/pubs/GotoTOMS2.pdf
[4] 
http://www.cs.utexas.edu/users/pingali/CS378/2008sp/papers/gotoPaper.pdf