I might be persuaded to see things from your point of view if you can show that Julia's tensors meet my criteria. I might be wrong about this - I do not know much about Julia and it might very well be capable of this. But these are the kinds of things that if it were capable of, it would advertise. If it can do it I'll apologize and change the line to show Julia in a more positive light.

The reason why I started talking about inlining of monads here is because ultimately they are just functions, and Spiral's tensors are not part of the language, but just functions as well. So if it cannot do monads then it probably cannot do tensors to satisfaction either.

It was pointed out to me that tensor slicing could serve as substitute for partial application. What I am wondering next is the degree of flexibility Julia's tensors have in the context of GPU kernels. Let me illustrate this with a short example. The following is how the inplace map Cuda kernel is made in Spiral. It seems simple, but I'll go at it from the top to show the characteristics and flexibility that Spiral's tensors have.

met map' w f in out =
inl in, out = zip in, zip out
assert (in.dim = out.dim) "The input and output dimensions must be equal."
inl in = flatten in |> to_dev_tensor
inl out = flatten out |> to_dev_tensor
inl in_a :: () = in.dim

inl blockDim = 128
inl gridDim = min 64 (divup (s in_a) blockDim)

w.run {
blockDim gridDim
kernel = cuda // Lexical scoping rocks.
grid_for {blockDim gridDim} .x in_a {body=inl {i} ->
inl out = out i
inl in = in i
out .set (f in.get out.get)
}
}

The first line is `inl in, out = zip in, zip out`. Since the inputs to to the map function can be arbitrary data structures, the `zip` iterates over it and zips the tensors in it into a single one.

In Spiral the tensors have a tuple of arrays layout by default, and it is absolutely trivial to switch between AOT and TOA representations. In fact you can mix and match tensors that have both layouts internally. Zipping tensors in Spiral just merges them into one with the TOA layout though the subtensors could be AOT.

Dealing with these layout transformations is a significant issue in numerical computation that Spiral solves completely. The reason why I have an impression that Julia cannot do this is not because I know for sure. Rather I do not think this can be done within the confines of a parametric type system. Rather I think it would require an intensional type system like the Spiral has.

inl in = flatten in |> to_dev_tensor
inl out = flatten out |> to_dev_tensor

`flatten` checks that the tensor is contiguous and flattens it into a 1d tensor. The `to_dev_tensor` is just some formality that I could not get rid of. It takes the pointers out from the references holding them which allows the tensors to be captured lexically before being passed into the kernel. Without this a type error would occur.

kernel = cuda // Lexical scoping rocks.
grid_for {blockDim gridDim} .x in_a {body=inl {i} ->
inl out = out i
inl in = in i
out .set (f in.get out.get)
}

The actual kernel itself is quite small and trivial. `cuda` is just a parser abbreviation for `inl {blockDim gridDim} ->`. In other words it is not a special language feature, but a regular function.

When I was doing this in F#, I had to not pass in every single argument of the function by hand - meaning the tensor pointers, their offsets, their sizes, their dimension ranges for each and every tensor, I also had to make wrapper classes on top of that. If a map operation needed more arguments, then I would have needed to copy paste a kernel, adjust it and the wrapper and repeat the testing process all over again, all this with very little type safety.

Spiral is not quite on the level of Futhark in terms of ergonomics, but it is a vast improvement over what came before. Note that there is no need for a type annotations anywhere - the function is as generic as it could possibly be in a statically typed language. Because of inlining guarantees all of this has zero overhead.

Compared to Spiral, where do Julia and its tensors stand on the axis of generality? Would it be possible to write a simple map as simply as this?