Hi All,

The Dlang multidimensional range type, ndslice, is a struct composed a an iterator, lengths and possibly strides. It does not own memory and does not know anything about its content. ndslice is a faster and extended version of numpy.ndarray.

After some work on commercial projects based on Lubeck[1] and ndslice I figure out what API and memory management is required to make Dlang super fast and math friendly in the same time.

The concept is the following:
1. All memory is managed by a global BetterC thread safe ARC allocator. Optionally the allocator can be overloaded. 2. User can take an internal ndslice to use mir.ndslice API internally in functions.
2. auto matrixB = matrixA; // increase ARC
3. auto matrixB = matrixA.dup; // allocates new matrix
4. matrix[i] returns a Vec and increase ARC, matrix[i, j] returns a content of the cell.
5. Clever `=` expression based syntax. For example:

   // performs CBLAS call of GEMM and does zero memory allocations
   C = alpha * A * B + beta * C;

`Mat` and other types will support any numeric types, PODlike structs, plus special overload for `bool` based on `bitwise` [2].

I have a lot of work for next months, but looking for a good opportunity to make Mat happen.

For contributing or co-financing:
Ilya Yaroshenko at
gmail com

Best Regards,

[1] https://github.com/kaleidicassociates/lubeck
[2] http://docs.algorithm.dlang.io/latest/mir_ndslice_topology.html#bitwise [3] http://www.netlib.org/lapack/explore-html/d1/d54/group__double__blas__level3_gaeda3cbd99c8fb834a60a6412878226e1.html#gaeda3cbd99c8fb834a60a6412878226e1

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