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End of a compiler optimisation

Posted on December 4, 2023 by Troels Henriksen

In this post I will talk about a somewhat unusual optimisation that the Futhark compiler has been performing for a long time. This post is written just before that optimisation is removed.

As I’ve written about now and then, the Futhark compiler is not a parallelising compiler. It expects you, the honoured programmer, to use the vocabulary of data parallelism to express your program, but it will not try to turn your sequential loops into parallel code. This is formalised through the solemn oath of a parallel cost model.

Futhark thus encourages a style of programming where the programmer maximises the amount of application parallelism. In practice, however, our computers are not infinitely parallel, and beyond a certain point, additional parallelism leads to no improvement in utilisation, but only additional overhead. Essentially all parallelism involves overhead (even if minuscule in some cases), and parallelism beyond what is necessary to saturate the machine is detrimental to performance.

Since the Futhark compiler is supposed to generate code that is fast in practice, on real computers, it often decides to sequentialise parts of a parallel program, in order to execute it with less overhead. Also, when we first developed the compiler, we did not have a parallel code generator at all, and so needed a transformation that could turn all those maps and reduces and whatnot into sequential loops. We call this sequentialisation. Consider sequentialising the following small expression:

let B = map (\x -> x + 2) A

It is immaterial whether we are sequentialising this because we are using a sequential compiler backend, or because we are deep inside another parallel computation and this level of parallelism is deemed unnecessary. In either case, the compiler will turn it into a a loop that looks like this, assuming A is of size n:

let B_initial = replicate n 0
let B = loop B = B_initial for i < n do
          let x = A[i]
          let tmp = x + 2
          let B[i] = tmp
          in B

This makes use of in-place updates through uniqueness types to express the sequentialised code efficiently. (In fact, the actual compiler representation does not use replicate, which requires initialising every element,, but a special scratch construct that creates an array with unspecified contents, by allocating memory without initialising it.)

The basic sequentialisation rule for map is to create an output array, loop over the input array, recursively sequentialise the lambda function whatever it might be, and then store the result in the output array at the proper location. But consider what happens when we nest maps:

let B = map (\a -> map (\y -> x + 2) a) A

Applying sequentialisation we get this, assuming A has shape [n][m]:

let B_initial = replicate n (replicate m 0)
let B = loop B = B_initial for i < n do
          let a = A[i]
          let tmp1_initial = replicate m 0
          let tmp1 = loop tmp1 = tmp1_initial for j < m do
                       let x = a[j]
                       let tmp2 = x + 2
                       let tmp1[j] = tmp2
                       in tmp1
          let B[i] = tmp1
          in B

There is inefficiency afoot. For each iteration of the outer loop, an array tmp1 is constructed, corresponding to the result of the innermost map. This tmp1 is then copied into B[i]. Since accessing memory is expensive, this redundant copy is not great. It would be much better if the result of the innermost map was constructed in-place, so no copy was necessary:

let B_initial = replicate n (replicate m 0)
let B = loop B = B_initial for i < n do
          let a = A[i]
          let B = loop B_inner = B for j < m do
                    let x = a[j]
                    let tmp2 = x + 2
                    let B_inner[i,j] = tmp2
                    in B_inner
          in B

For reasons now lost to time, we decided to address this not by changing sequentialisation to more directly deal with nesting, but by introducing a more general optimisation, called in-place lowering because it pushes in-place updates down into loop nests. I suppose we thought user-written code might also benefit from this. The idea was to recognise code of the form

let r =
  loop r1 = r0 = for i < n do
    let a = r1[i]
    let r1[i] = ... a ...
    in r1
let y[k] = r in

and turn it into

let x0 = y with [k] <- r0
let y =
  loop x = x0 = for i < n do
    let a = a[k,i]
    let x[k,i] = ... a ...
    in x
let y = x0

For the above transformation to be valid, a long list of conditions must be fulfilled. For example, r must not be consumed after the original in-place update (since now that would involve consuming y[k]). Another is that the array y (or its aliases) may not be used inside the body of the loop, as that could violate data dependencies. There are more, and they are quite fiddly. And to our mild embarrassment, we never actually managed to check all of them. Nevertheless, this optimisation existed since early 2015, and managed to speed up at least some programs, some of the time.

During our initial work on the in-place forwarding optimisation, we realised it was a somewhat limited implementation of a general idea: avoiding copies by constructing values in-place. A mere seven years later, Philip Munksgaard finished his PhD on (among other things) array short circuiting, which is exactly a general implementation of that idea. And only a year after Philip finished his work, I remembered that short circuiting in principle makes in-place lowering redundant, and decided to remove it. Both in-place lowering and array short circuiting are very complicated program transformations, and I am unsure whether short circuiting actually handles all the (somewhat ad hoc) cases detected by in-place lowering. When comparing the performance obtained when disabling the latter (which is what actually matters), it seems there is no real impact. And so, after over eight years of service, an optimisation I added in the first year of my PhD studies is finally gone.

Good riddance; I really disliked having to fix bugs in that pass.

I still want to improve our sequentialisation transformation some day, though.