Mir vs. Numpy: Reworked!

[data pulverizer]

On Monday, 7 December 2020 at 02:14:41 UTC, 9il wrote:
> I don't know. Tensors aren't so complex. The complex part is a 
> design that allows Mir to construct and iterate various kinds 
> of lazy tensors of any complexity and have quite a universal 
> API, and all of these are boosted by the fact that the 
> user-provided kernel(lambda) function is optimized by the 
> compiler without the overhead.

I agree that a basic tensor is not hard to implement, but the 
specific design to choose is not always obvious. Your benchmarks 
shows that design choices have a large impact on performance, and 
performance is certainly a very important consideration in tensor 
design.

For example I had no idea that your ndslice variant was using 
more than one array internally to achieve its performance - it 
wasn't obvious to me.

I think literature that discuss various design choices and 
approaches would be useful and informative. There is plenty of 
literature on creating tree structures, linked lists, stacks, 
queues, hash tables and so forth, but virtually nothing on tensor 
data structures. It isn't as if implementing a linked list is any 
more complex than a tensor. I just think it's a bit strange that 
there is so little on the topic - given the widespread use of 
tensors in computational science.