A compute object

Let's create a tensor, and a simple element-wise op on the CPU:

• $A$ is the name of our tensor.

• $\mathrm{ad}(A)$ is the set of addresses that will hold elements of $A$ in memory.

• $S_A$ is the shape of $A$.

• $I(A)$ is the set of logical indices of the tensor $A$.

• In particular, we have a layout map $l_A$ from the address set of $A$ to the logical indices of $A$.

• We also have a value map $v_a$ that assigns indices of $A$ to real numbers.

• Finally, the map $q$ is our element-wise function.

The goal of this operation is to turn $v_a$ into $q\circ v_a$.

Please think of $I(A)$ simply as the indices of the mathematical object $A$, not as the index that is used for array access, which has the property that
addr(index) = base_addr + index * element_width.
$I(A)$ does not need to hold such properties. Also, notice that all mathematical objects are immutable.

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No cost operations on compute objects

1. Memory layout rearrangement.
   Okay this one is exactly no cost, in the sense that moving stuff around in memory obviously requires copy operations, but before initialising the tensor, we can choose beforehand what memory layout is optimal, so we can basically pretend that its free of cost to change memory layout at this stage.

(A, \operatorname{ad}(A), S_{A}, I(A), l_{A}: \operatorname{ad}(A) \to I(A), v_{a}: I(A) \to
\mathbb{R}, q: \mathbb{R} \to \mathbb{R})

(A, \operatorname{ad}(A), S_{A}, I(A), l'{A}: \operatorname{ad}(A) \to I(A), v: I(A) \to
\mathbb{R}, q: \mathbb{R} \to \mathbb{R})

(A, \operatorname{ad}(A), S_{A}, I(A), l_{A}: \operatorname{ad}(A) \to I(A), v_{a}: I(A) \to
\mathbb{R}, q: \mathbb{R} \to \mathbb{R})

(A_{1}, \operatorname{ad}(A_{1}), S_{A_{1}}, I(A_{1}), l_{A_{1}}: \operatorname{ad}(A_{1}) \to I(A_{1}), v_{a_{1}}: I(A_{1}) \to
\mathbb{R}, q: \mathbb{R} \to \mathbb{R})

(A_{2}, \operatorname{ad}(A_{2}), S_{A_{2}}, I(A_{2}), l_{A_{2}}: \operatorname{ad}(A_{2}) \to I(A_{2}), v_{a_{2}}: I(A_{2}) \to
\mathbb{R}, q: \mathbb{R} \to \mathbb{R})