Current value semantic / copy-on-assignment is not good enough performance-wise. It requires unsafe all over the place which is not ergonomic at all.

I tried copy-on-write as well, in-depth monologue discussion in this issue #157. You can implement COW with atomic reference counting or a shared/non-shared boolean but it has a few problems detailed here:

• Refcounting/isShared troubles when wrapped in a container
• Performance predictability "when will it copy or share", compared to always copy or always share
• Workaroundability: since = is overloaded, it is non-trivial to avoid COW, let a = b.unsafeView won't work

So Arraymancer will move to reference semantics (share Tensor data by default, copy must be made explicit).

Benefits:

• CudaTensor already had this semantic
• No need to sprinkle unsafeSlice all over the place for performance
  • All the unsafe proc can be removed
  • Much less code to maintain
• Numpy and Julia already work like this
• Most copies are explicit (except asContiguous and reshape
  • debugging copy issues will be just grep clone *.nim

Disadvantages

• Sharing is implicit: users might forget to use clone and share data by mistake.
  • debugging sharing issues will be harder than grep unsafe *.nim

In the wild

Numpy and Julia have reference semantics, Matlab and R have copy-on-write.