Scans and reductions
reduce function reduces an array to a single value by conceptually inserting a binary operator (or any two-parameter function) between each element.
reduce (+) 0 [1, 2, 3, 4] == [1 + 2 + 3 + 4] == 10
We can use them to define a function for computing averages:
There are some restrictions to enable parallel execution. In an expression
reduce f ne xs, the function
f must be associative and have
ne as neutral element. Intuitively, associativity means that we can move around the parantheses in an application:
f (f x y) z == f x (f y z)
It’s a bit easier to understand if we write the function as an infix operator instead:
(x + y) + z == x + (y + z)
ne being a neutral element means that it does not affect the result of the function:
f x ne == f ne x == x
As a simple example, 0 is the neutral element for addition, and 1 for multiplication.
If we pass
reduce a function that is not associative, or does not have the provided neutral element, we will get wrong results at run-time. What’s worse, the compiler will not be able to detect that we messed up (it’s actually impossible in general), however techniques exist for testing associativity empirically.
Scans (also called prefix sums) are similar to reductions, but rather than producing a single result, they produce an array of the same size as the input, where each element is conceptually a reduction of a prefix of the array:
scan (+) 0 [1, 2, 3, 4] == [reduce (+) 0 , reduce (+) 0 [1,2], reduce (+) 0 [1,2,3], reduce (+) 0 [1,2,3,4]] == [1, 3, 6, 10]
Somewhat surprisingly, these can also be efficiently computed in parallel, and have the same restrictions with respect to associativity and a neutral element as
reduce. For now, scans may look a bit exotic, and they certainly are, but we’ll return to them in other examples, as they are an important building block in advanced parallel algorithms.