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.
