6. Fusion and List Homomorphisms

In this chapter, we outline the general SOAC reasoning principles that lie behind both the philosophy of programming with arrays in Futhark and the techniques used for allowing certain programs to have efficient parallel implementations. We shall discuss the reasoning principles in terms of Futhark constructs but introduce a few higher-order concepts that are important for the reasoning.

We first discuss the concept of fusion, which aims at eliminating intermediate arrays while still allowing the Futhark programmer to express an algorithm using simple SOACs and their associated reasoning principles.

We then introduce the concept of list homomorphism through a few examples.

6.1. Fusion

Fusion aims at reducing the overhead of unnecessary repeated control-flow or unnecessary temporary storage. In essence, fusion is defined in terms of a number of fusion rules, which specify how a Futhark (intermediate) expression can be transformed into a semantically equivalent expression.

The rules make use of the auxiliary higher-order functions for, for instance, function composition, presented in Section 2.6.

The first fusion rule, \(F1\), which says that the result of mapping an arbitrary function f over the result of mapping another arbitrary function g over some array a is identical to mapping the composed function f <-< g over the array a. The first fusion rule is also called map-map fusion and can simply be written

map f <-< map g = map (f <-< g)

Given that f and g denote the Futhark functions \x -> e and \y -> e', respectively (possibly after renaming of bound variables), the function product of f and g, written f <*> g, is defined as \(x,y) -> (f x, g y).

Now, given functions f:a->b and g:a->c, the second fusion rule, \(F2\), which denotes horizontal fusion, is given by the following equation:

(map f <*> map g) <-< dup = map ((f <*> g) <-< dup)

Here dup is the Futhark function \x -> (x,x).

The fusion rules that we have presented here generalise to functions that take multiple arguments by applying zipping, unzipping, currying, and uncurrying strategically. Notice that due to Futhark’s strategy of automatically transforming arrays of tuples into tuples of arrays, the applications of zipping, unzipping, currying, and uncurrying have no effect at runtime.

Futhark applies a number of other fusion rules, which are based on the fundamental property that Futhark’s internal representation is based on a number of composed constructs (e.g., named scanomap and redomap). These constructs turn out to fuse well with map.

6.2. Parallel Utility Functions

For use by other algorithms, a set of utility functions for manipulating and managing arrays is an important part of the tool box. We present a number of utility functions here, ranging from finding elements in an array to finding the maximum element and its index in an array.

6.2.1. Finding the Index of an Element in an Array

We device two different functions for finding an index in an array for which the content is identical to some given value. The first function, find_idx_first, takes a value e and an array xs and returns the smallest index i into xs for which xs[i] = e:

-- Return the first index i into xs for which xs[i] == e
def find_idx_first [n] (e:i32) (xs:[n]i32) : i32 =
  let es = map2 (\x i -> if x==e then i else n) xs (iota n)
  let res = reduce i32.min n es
  in if res == n then -1 else res

The second function, find_idx_last, also takes a value and an array but returns the largest index i into xs for which xs[i] = e:

-- Return the last index i into xs for which xs[i] == e
def find_idx_last [n] (e:i32) (xs:[n]i32) : i32 =
  let es = map2 (\x i -> if x==e then i else -1) xs (iota n)
  in reduce i32.max (-1) es

The above two functions make use of the auxiliary functions i32.max and i32.min.

6.2.2. Finding the Largest Element and its Index in an Array

Futhark allows for reduction operators to take tuples as arguments. This feature is exploited in the following function, which implements a homomorphism for finding the largest element and its index in an array:

-- Find the largest integer and its index in an array
def MININT : i32 = -10000000

def mx (m1:i32,i1:i32) (m2:i32,i2:i32) : (i32,i32) =
  if m1 > m2 then (m1,i1) else (m2,i2)

def maxidx [n] (xs: [n]i32) : (i32,i32) =
  reduce mx (MININT,-1) (zip xs (iota n))

The function is a homomorphism [Bir87]: For any \(x\) and \(y\), and with \(++\) denoting array concatenation, there exists an associative operator \(\oplus\) such that

\[\kw{maxidx}(x \pp y) = \kw{maxidx}(x) \oplus \kw{maxidx}(y)\]

The operator \(\oplus = \kw{mx}\). We will leave it up to the reader to verify that the maxidx function will operate efficiently on large inputs.

6.3. Radix Sort Revisited

A simple radix sort algorithm was presented already in Section 5.6.1. In this section, we present two generalized versions of radix sort, one for ascending sorting and one for descending sorting. As a bonus, the sorting routines return both the sorted array and an index array that can be used to sort an array with respect to a permutation obtained by sorting another array. The generalised ascending radix sort is as follows:

-- Store elements for which bitn is not set first
def rs_step_asc [n] ((xs:[n]u32,is:[n]i32),bitn:i32) : ([n]u32,[n]i32) =
  let bits1 = map (\x -> (i32.u32 (x >> u32.i32 bitn)) & 1) xs
  let bits0 = map (1-) bits1
  let idxs0 = map2 (*) bits0 (scan (+) 0 bits0)
  let idxs1 = scan (+) 0 bits1
  let offs  = reduce (+) 0 bits0    -- store idxs1 last
  let idxs1 = map2 (*) bits1 (map (+offs) idxs1)
  let idxs  = map (\x->x-1) (map2 (+) idxs0 idxs1)
  in (scatter (copy xs) idxs xs,
      scatter (copy is) idxs is)

-- Radix sort - ascending
def rsort_asc [n] (xs: [n]u32) : ([n]u32,[n]i32) =
  let is = iota n
  in loop (p : ([n]u32,[n]i32)) = (xs,is) for i < 32 do

And the descending version as follows:

-- Store elements for which bitn is set first
def rs_step_desc [n] ((xs:[n]u32,is:[n]i32),bitn:i32) : ([n]u32,[n]i32) =
  let bits1 = map (\x -> (i32.u32 (x >> u32.i32 bitn)) & 1) xs
  let bits0 = map (1-) bits1
  let idxs1 = map2 (*) bits1 (scan (+) 0 bits1)
  let idxs0 = scan (+) 0 bits0
  let offs  = reduce (+) 0 bits1    -- store idxs0 last
  let idxs0 = map2 (*) bits0 (map (+offs) idxs0)
  let idxs  = map (\x->x-1) (map2 (+) idxs1 idxs0)
  in (scatter (copy xs) idxs xs,
      scatter (copy is) idxs is)

-- Radix sort - descending
def rsort_desc [n] (xs: [n]u32) : ([n]u32,[n]i32) =
  loop (p : ([n]u32,[n]i32)) = (xs,iota n) for i < 32 do

Notice that in case of identical elements in the source vector, one cannot simply implement the ascending version by reversing the arrays resulting from calling the descending version.

6.4. Finding the Longest Streak

In this section, we shall demonstrate how to write a function for finding the longest streak of increasing numbers. Here is one possible implementation of the function:

-- Longest streak of increasing numbers
def streak [n] (xs: [n]i32) : i32  =
  -- find increments
  let ys = rotate 1 xs
  let is = (map2 (\x y -> if x < y then 1 else 0) xs ys)[0:n-1]
  -- scan increments
  let ss = scan (+) 0 is
  -- nullify where there is no increment
  let ss1 = map2 (\s i -> s*(1-i)) ss is
  let ss2 = scan max 0 ss1
  -- subtract from increment scan
  let ss3 = map2 (-) ss ss2
  let res = reduce max 0 ss3
  in res

The following derivation shows how the algorithm works for a particular input, namely when stream is given the argument array [1,5,3,4,2,6,7,8], in which case the algorithm should return the value 3:

xs = 1 5 3 4 2 6 7 8
ys = 5 3 4 2 6 7 8 1
is = 1 0 1 0 1 1 1  
ss = 1 1 2 2 3 4 5  
ss = 0 1 0 2 0 0 0  
ss2 = 0 1 1 2 2 2 2  
ss3 = 1 0 1 0 1 2 3  
res = 3              

In Section 7.1.1 we present a simpler algorithm, which builds directly on the concept of a so-called segmented scan.