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Designing a Functional Language for GPU Execution

Posted on September 3, 2016 by Troels Henriksen

The Futhark programming language is high-level, data-parallel and hardware-agnostic. It is intended to be used both for hand-programming and as a target for code generation. To demonstrate that functional programming can deliver high parallel performance, we have implemented an optimising compiler that generates code for GPU execution. We spend most of our time working on how such a compiler should be constructed, and which functional invariants we can exploit to perform optimisation. Less time is spent designing the Futhark source language itself. This stands to reason: there are not many compilers for functional language that can generate efficient GPU code, but there has been a lot of work on designing convenient and elegant functional languages. Ideally, we want to innovate on the former, and copy from the latter.

Unfortunately, we cannot merely copy-paste a Standard ML parser into our compiler frontend and be done with it. GPUs are a rather hostile environment, and such sophisticated facilities as memory allocation, function pointers, recursion, and branching are unavailable or inefficient. It might be possible to compile a conventional functional language to GPUs, but performance would likely not be good (Harlan makes a brave effort, however). The market for nice expressive moderately-fast functional languages is already pretty crowded, so Futhark targets a different market: a smaller language with fewer features, but where most language constructs are (or can be) executed efficiently. Put simply, Futhark is not worth using unless it is fast. This inspires two language design constraints:

For reasons of time, we are mostly hacking on compiler optimisations, not doing language design. This means that Futhark has grown rather slowly, but it has changed, at least superficially. As an example, here’s how we would once have written a matrix-matrix multiplication:

let [[i32]] main([[i32]] x, [[i32]] y) =
  map(\[i32] ([i32] xr) ->
        map(\i32 ([i32] yc) ->
              reduce(+, 0, zipWith(*, xr, yc)),

There is a Haskell-like notation for array types, and C-style type indications, where the parameter type precedes the parameter name. The parallel constructs (map, reduce, transpose) require parentheses around their comma-separated arguments, just like any other function call - again, like C.

Here is what it looks like now:

let main(x: [n][m]i32, y: [m][p]i32): [n][p]i32 =
  map (\xr ->
         map (\yc -> reduce (+) 0 (zipWith (*) xr yc))
             (transpose y))

Type annotations are now optional except for top-level functions, function application is by juxtaposition, and the array type syntax is different. We also support shape declarations that express how the types of inputs must relate to each other and to the result. This is not proper dependent typing - again, we keep it simple - but is asserted through run-time checks, and used by the compiler to perform optimisations.

We also used to not have pattern matching for function parameters, or type aliases. To wit, observe the initial version of a vector addition function from an N-body benchmark:

let {f32, f32, f32} vec_add({f32, f32, f32} v1, {f32, f32, f32} v2) =
  let {x1, y1, z1} = v1
  let {x2, y2, z2} = v2
  in {x1 + x2, y1 + y2, z1 + z2}

We also used curly braces for tuples in those days. Now the function looks like this:

let vec_add((x1, y1, z1): vec3) ((x2, y2, z2): vec3): vec3 =
  (x1 + x2, y1 + y2, z1 + z2)

Essentially, we are moving towards an SML/Haskell/F# feel for Futhark, but with the restrictions that result from our demand for performance and our uncooperative target platform. Concretely, as time permits, we plan to extend Futhark with:

  1. An SML-style module system. We already have a simple implementation of structures and the beginning of signature support. All that is needed is to finish signatures and add functors.
  2. Some form of statically resolvable ad-hoc polymorphism. I’m thinking modular type classes, which are also being considered for Successor ML.
  3. Proper type inference, even for top-level functions. This is hopefully easy to implement, although our uniqueness types may make things interesting.

These features would enable a style of modular programming that can still be compiled efficiently, and would not be too hard to implement. We can then move on to more advanced features, like:

  1. True higher-order functions (or a convincing imitation). These are typically implemented with function pointers, which are tricky (slow/impossible) on GPUs. Perhaps a solution could be a type system feature that ensures that higher-order values correspond to a (statically known) lambda term, but which might have any lexical closure. For example, this would be permitted:

    let makeAdder x = \y -> y + x

    because the caller of makeAdder x would always know the “form” of the function being returned. Meanwhile, this would not:

    let adderOrSubber b x = if b then (\y -> y - x) else (\z -> y + x)

    because the form of the returned function depends on a dynamic decision. For this particular function, a workaround could be:

    let adderOrSubber b x = \y -> y + if b then -x else x

    Note that the function composition operator obeys this rule:

    let compose f g = \x -> f (g x)

    Except that’s a function, not an operator, which reminds me…

  2. User-defined operators. I like how Haskell has made infix operators lexically distinct, and I also like how F# defines operator priority and associativity based on its constituent characters.

Of course, it’s also worth discussing smaller changes, such as whether our notation for anonymous functions is too verbose (it is), and whether we should have syntax for common cases like ranges and array comprehensions (probably). If you like working on language design or making things go fast, why not contribute? The Futhark compiler frontend, which processes source programs and converts them into the core language, is not big and fairly easy to understand.