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How Futhark represents values at runtime

Posted on August 2, 2021 by Troels Henriksen

In an almost literal sense, Futhark is an elaborate collection of smoke and mirrors that lets the programmer think they are writing a high-level program with polymorphism, higher-order functions, and all the other bells and whistles. But really, they are actually writing a first-order monomorphic C program, full of unboxed data and tight loops. Nowhere is this more clear than in how Futhark values are actually represented in memory at runtime. In this post I will describe what that representation looks like. Apart from perhaps being interesting to other language designers, it is also useful knowledge for Futhark programmers who wish to ensure that they are writing efficient code. Some of the concerns described below also justify some of Futhark’s more annoying restrictions. Sometimes the smoke clears or a mirror breaks and you can see the ghastly reality that normally lies hidden.

Traditionally, functional languages use a uniform value representation where all values are represented by a pointer to an object on the heap. I admire the elegant simplicity of this approach - particularly how it makes polymorphic functions straightforward to represent in compiled code. If everything is just a pointer, you can freely shuffle those around, without worrying about what they point to! The drawbacks are extremely high pressure on the memory allocator, potentially significant overhead in memory usage, and a loss of locality. For some programs, these are not major problems - those that mainly perform symbolic processing, such as compilers, spend their time chasing pointers anyway. But for Futhark, all three are problematic:

  1. Unpredictable dynamic allocation is a poor fit for massive parallelism, as the allocator becomes a synchronisation bottleneck.

  2. Memory bandwidth is usually the bottleneck in Futhark’s problem domain, so inefficient data representations directly impacts performance.

  3. Locality-based optimisations are crucial for exploiting the memory hierarchy of modern machines. Unless we can know for sure that neighbouring values in an array are also neighbours in memory (and not just pointers to who-knows-where), we don’t have a chance of doing such optimisations.

Instead, Futhark’s value representation is focused on unboxed values, where values of any built-in scalar type such as i32 are represented merely as themselves, located on the stack or in registers. This has major ramifications for compilation scheme as a whole - since there is no uniform representation, polymorphic functions must be monomorphised, where a version is generated for distinct each type the function is used with. This leads to increases in code size and prevents separate compilation, but since Futhark is not aimed at large programs, it is not a high price to pay. A more subtle constraint is that it forces us to use values whose total size can be determined statically, with arrays as the single example of dynamically-sized values in the languages. This is in contrast to most (all?) other functional languages, where you can easily define a recursive data type whose values can be of arbitrary size at runtime. For this reason, Futhark does not support recursive data types.

Scalar types

Futhark’s main code generator produces C code, so we can explain the value representation in terms of the C that is generated. A Futhark function that takes an argument of type f64 will be translated into a C function that takes an argument of type double. No trickery or indirection, although we’re assuming that the function is not just inlined entirely. We do not distinguish signed and unsigned types - to the compiler, i32 and u32 are exactly the same.

Tuples are merely flattened and then represented by their individual components - there are no tuple objects at runtime, or even in our intermediate representation. A function that takes an argument of type (f64,f64) will result in a C function that takes two arguments of type double. This has one performance implication: whenever you pass a tuple to a function, the entire tuple is copied (except any embedded arrays, which are always passed by reference as we’ll see below). This is in contrast to most other functional languages, where tuples are boxed, and so passing them around involves only copying a pointer. Due to the compiler’s heavy use of inlining, this is rarely a problem in practice, but it can be a concern when using the loop construct with a large tuple as the loop variant parameter.

Records and sum types are handled by turning them into tuples, and then processing the tuples as described above. Records are turned into tuples by simply sorting their fields and discarding the labels. The translation of sum types is more tricky and less efficient. As a starting point, a sum type is turned into a tuple containing all the payload components in order, prefixed with an i8 tag to identify the constructor. For example,

#foo i32 bool | #bar i32

would be turned into a tuple

(i8, i32, bool, i32)

and the value

#foo 42 false

would be represented as

(1, 42, false, 0)

where #foo is assigned the tag 1 because it is alphabetically after #bar.

This is obviously fairly inefficient, especially for large types. In practice, the compiler does a bit of deduplication as well. If multiple constructors have payload elements of the same type, we assign them to the same elements in the result tuple, so the representation of the above sum type would actually be the following:

(i8, i32, bool)

This can still have significant performance implications. The types must be the same for deduplication to take place - despite i32 and f32 being of the same size, they cannot be assigned the same tuple element. What would the element of that tuple be? This is because our intermediate representation, for simplicity, does not have a C-like union type. Matters are even graver when the payload is an array. The type

#foo [n]i32 | #bar [n]i32

will be efficiently represented as

(u8, [n]i32)


#foo [n]i32 | #bar [n]f32

will become

(u8, [n]i32, [n]f32)

which is not great - and this cannot be resolved simply by adding C-like union types. Take caution when you use sum types with large arrays in their payloads.

Array types

Arrays are the only Futhark values that are boxed - that is, are stored on the heap. They are also the only values that have a dynamic size, but crucially that size (the shape of the array) is known “at the top”, without having to look at the contents of the array - and is in fact usually known far before the array elements themselves are computed. This is not the case for recursive data types as we know them from mainstream functional languages. This property is what allows the Futhark compiler to aggregate, combine, and hoist individual small allocations out into large bulk allocations, which avoids pressure on the memory allocator.

The elements of an array are unboxed, stored adjacent to each other in memory. This means we have zero memory overhead except for the minuscule amount needed to track the shape of the array. Computing an element address is as efficient as can be: just multiply the index with the statically known element size. At the surface language level, Futhark may appear to support “arrays of arrays”, and this is indeed a convenient aspect of its programming model, but at runtime multi-dimensional arrays are stored in flattened form. A value of type [x][y]i32 is laid out in memory simply as one array containing x*y integers. (I’m skipping over some details here, see the fine print below.) This is one reason why Futhark does not allow jagged or irregular arrays, such as [[1,2], [3]] - while they are straightforward to handle if arrays could contain pointers to other arrays, they require complex supporting machinery such as flag arrays if you want a completely unboxed representation.

Since arrays cannot contain other arrays, memory management only has to be concerned with one level of indirection. In practice, it means that Futhark can use straightforward reference counting to keep track of when to free the memory backing an array, as circular references are not possible. Further, since arrays tend to be large and relatively few in number, the usual performance impact of naive reference counting is not present.

So that’s (multi-)dimensional arrays of scalars. But how does Futhark represent arrays of tuples, such as [](i32,bool)? One option is the obvious dense one:

0           4      5           9      10
|     i32   | bool |     i32   | bool |...

This is conceptually simple, but has major downsides. The main one is that the second i32 value will start 5 bytes into the array - likely an unaligned address. In practice, almost no languages do it this way. Even C will normally insert padding bytes in structs to ensure alignment, which might lead to representations such as this one:

0           4            8           12           16
|     i32   |    bool    |     i32   |    bool    |...

Now every integer is located on an address divisible by four, but with 3 bytes of useless padding for every element - a substantial overhead. Further, this representation makes it expensive to do projection. What if some part of our program only needs the Boolean parts? Each memory transaction will fetch an entire consecutive chunk of memory (say, a cache line) and if the chunk contains data we do not need, then we are wasting precious memory bandwidth. This is a common problem in high performance computing, and the usual solution is to use a so-called structure of arrays representation. Instead of representing arrays of structs (or in our case, tuples), we maintain a distinct array for every component of the element tuples:

0           4            8           12           16
|     i32   |     i32    |     i32   |     i32    |...

0      1      2      3
| bool | bool | bool | ...

This gives us two dense, properly aligned arrays of primitives. And if we only need the booleans, then the code needs only traverse that array. In Futhark terms, an array [n](a,b,c) is at runtime represented as the tuple ([n]a,[n]b,[n]c). This has some significant implications. For example, zip and unzip are very cheap, as the actual runtime representation is in always “unzipped”, so these functions don’t actually have to do anything. This representation was put in place in the very early days of the Futhark compiler (before it was even called Futhark) and has served us very well.

This desire to ultimately deal only with arrays of scalars is why we use such a relatively inefficient encoding of sum types. By turning everything into tuples, and then arrays of tuples into arrays of scalars, we end up with extremely simple memory layouts. It’s not yet clear to us how to improve sum types without significant cost (whether in complexity or performance) elsewhere.

Finally, I’ll mention that the representation above is not merely the memory layout (and in fact, see below), but also the value model used throughout our intermediate representation. Monomorphisation, converting records and sum types, and turning arrays of tuples into tuples of arrays is done as one of the first steps of our compilation pipeline. The various optimisation passes do not deal with tuples at all - just scalars and arrays of scalars. I think this simplicity is key to why we’ve been able to implement such aggressive optimisations.

The fine print

I couldn’t decide whether the following details were significant or not, but they impeded the narrative of the main body of text, so to the bottom they went.

While functional compilers for languages such as OCaml and Haskell do have the notion of a uniform representation, they also put effort into unboxing intermediate results. If you write (x+y)*z in Haskell, it’s not actually going to heap-allocate the result of x+y just to immediately read it afterwards. (Well, depending on whether you’re adding together values of particularly fancy types.) For strict languages this kind of local unboxing is not so difficult, although Haskell being lazy also has to perform strictness analysis to ensure that it is not compromising on laziness, as thunks must be boxed. Most of these languages also provide some support for explicitly working with unboxed data (GHC even has polymorphism for unboxed values).

Futhark isn’t the only functional compiler to take the monomorphic whole-program optimising approach. MLton is a production-ready compiler for Standard ML that also does it this way, and while the compile times are certainly very long by most standards, they are not unusably so - for example, MLton can compile the 100k line MLKit in about 20 minutes.

While the description of arrays above is correct in that array elements are stored unboxed, I was intentionally vague about their exact layout. This is because the Futhark compiler is at liberty to arrange the precise element order in whatever way it wishes - whether row or column-major, or a mixture, depending on what it decides is most efficient given how the array is eventually used. It may even decide to duplicate an array and store it twice in two different layouts, in case it is simultaneously traversed in two different ways that cannot be efficiently handled with a single layout. Slicing operations such as A[:,0] (which extracts the first column of an array) may also lead to elements that are not consecutive in memory at all - again, it depends on how the compiler decided A should be stored in memory, and whether it decides to make the slice a copy rather than merely a delayed “view”.

Take care not to confuse the precise in-memory layout (which is an implementation detail) with the representation used for interacting with Futhark arrays via the C API, which always uses logical row-major ordering.