# An introduction to Data Oriented Design with Rust An introduction to Data Oriented Design with Rust

In the post we will investigate the main concepts of Data-oriented Design using Rust.

The source code for this example is available on Github.

## What is data-oriented design?

Data-oriented design is an approach to optimising programs by carefully considering the memory layout of data structures, and their implications for auto-vectorisation and use of the CPU cache. I highly recommend watching Mike Acton’s “Data-Oriented Design and C++” talk if you haven’t seen it already.

In this post we will cover 4 cases, using criterion for benchmarking. The cases are:

• Struct of arrays vs. array of structs
• The cost of branching inside a hot loop
• Linked List vs. Vector iteration
• The cost of dynamic dispatch vs. monomorphisation

## Struct of Arrays vs. Array of Structs

The Struct of Arrays vs. Array of Structs refers to two contrasting ways of organising entity data to be operated over.

For example, imagine we are writing a video game and we would like to have a Player struct with the following fields:

pub struct Player {
name: String,
health: f64,
location: (f64, f64),
velocity: (f64, f64),
acceleration: (f64, f64),
}


Then at each frame, we want to update the locations and velocities of all Players. We could write something like:

pub fn run_oop(players: &mut Vec<Player>) {
for player in players.iter_mut() {
player.location = (
player.location.0 + player.velocity.0,
player.location.1 + player.velocity.1,
);
player.velocity = (
player.velocity.0 + player.acceleration.0,
player.velocity.1 + player.acceleration.1,
);
}
}


This would be the usual object-oriented approach to this problem. The issue here is that in memory the structs are stored as follows (assuming no field re-ordering i.e. #[repr(C)]), on a 64-bit architecture each field will be 64 bits (8 bytes, so each Player is 64 bytes):

-- Vec<Player>
name  (pointer to heap)  -- Player 1
health
location0  (tuple split for clarity)
location1
velocity0
velocity1
acceleration0
acceleration1
name  (pointer to heap)  -- Player 2
location0
location1
velocity0
velocity1
acceleration0
acceleration1
...


Note the parts we want to operate on (locations, velocities and accelerations) are not stored contiguously across different Players. This prevents us from using vector operations to operate on multiple players at once (since they cannot be loaded in the same CPU cache line, usually ~64 bytes).

In contrast, the data-oriented approach is to design around this limitation and optimise for auto-vectorisation. Instead of using a struct per Player, we now use one struct for all Players and each Player has their values stored at their index in the separate attribute Vectors:

pub struct DOPlayers {
names: Vec<String>,
health: Vec<f64>,
locations: Vec<(f64, f64)>,
velocities: Vec<(f64, f64)>,
acceleration: Vec<(f64, f64)>,
}


Now we can do the same calculation as in the OOP case as follows:

pub fn run_dop(world: &mut DOPlayers) {
for (pos, (vel, acc)) in world
.locations
.iter_mut()
.zip(world.velocities.iter_mut().zip(world.acceleration.iter()))
{
*pos = (pos.0 + vel.0, pos.1 + vel.1);
*vel = (vel.0 + acc.0, vel.1 + acc.1);
}
}


In this case the memory layout is as follows:

-- DOPlayers
name1    -- names
name2
...
health1    -- health
health2
...
location1    -- locations
location2
...


The relevant fields are now stored contiguously. Given that each location tuple will be 16 bytes, we could now feasibly load 4 location tuples on the same cache line to operate on them simultaneously with SIMD instructions.

### Benchmark

Here are the results of the criterion benchmark for the above code (the full code and benchmark code is available in the Github repo):

Overall, we see that the data-oriented approach finishes in half the time. This would seem to be due to the data-oriented case operating on two Players at a time - we can confirm this by reviewing the compiled assembly.

Reviewing the output on Godbolt we see the following:

// Relevant OOP loop
.LBB0_2:
movupd  xmm0, xmmword ptr [rax + rdx + 32]
movupd  xmm1, xmmword ptr [rax + rdx + 48]
movupd  xmm2, xmmword ptr [rax + rdx + 64]
movupd  xmmword ptr [rax + rdx + 32], xmm0
movupd  xmmword ptr [rax + rdx + 48], xmm2
cmp     rcx, rdx
jne     .LBB0_2

// ...
// Relevant DOP loop
.LBB1_7:
movupd  xmm0, xmmword ptr [rcx + rdx - 16]
movupd  xmm1, xmmword ptr [rax + rdx - 16]
movupd  xmmword ptr [rcx + rdx - 16], xmm1
movupd  xmm0, xmmword ptr [r9 + rdx - 16]
movupd  xmm1, xmmword ptr [rax + rdx - 16]
movupd  xmm0, xmmword ptr [rax + rdx]
movupd  xmmword ptr [rax + rdx - 16], xmm1
movupd  xmm1, xmmword ptr [rcx + rdx]
movupd  xmmword ptr [rcx + rdx], xmm1
movupd  xmm0, xmmword ptr [rax + rdx]
movupd  xmm1, xmmword ptr [r9 + rdx]
movupd  xmmword ptr [rax + rdx], xmm1
cmp     rsi, rdi
jne     .LBB1_7
test    r8, r8
je      .LBB1_5


We can see in the data-oriented case, the loop is unrolled to operate on two elements at once - resulting in the 50% speed up overall!

Addendum: As noted by /u/five9a2 on Reddit the above output is specifically for the default target, which is misleading since cargo bench uses the native target by default (i.e. all possible features on your CPU), so our benchmarks are not using the above assembly code.

By setting the compiler flag to -C target-cpu=skylake-avx512 to enable Skylake features, we get the following output:

// OOP loop
.LBB0_2:
vmovupd ymm0, ymmword ptr [rax + rdx + 32]
vaddpd  ymm0, ymm0, ymmword ptr [rax + rdx + 48]
vmovupd ymmword ptr [rax + rdx + 32], ymm0
cmp     rcx, rdx
jne     .LBB0_2

...
// DOP loop
.LBB1_19:
vmovupd zmm0, zmmword ptr [rsi + 4*rax - 64]
vaddpd  zmm0, zmm0, zmmword ptr [rcx + 4*rax - 64]
vmovupd zmmword ptr [rsi + 4*rax - 64], zmm0
vmovupd zmm0, zmmword ptr [rcx + 4*rax - 64]
vaddpd  zmm0, zmm0, zmmword ptr [r10 + 4*rax - 64]
vmovupd zmmword ptr [rcx + 4*rax - 64], zmm0
vmovupd zmm0, zmmword ptr [rsi + 4*rax]
vaddpd  zmm0, zmm0, zmmword ptr [rcx + 4*rax]
vmovupd zmmword ptr [rsi + 4*rax], zmm0
vmovupd zmm0, zmmword ptr [rcx + 4*rax]
vaddpd  zmm0, zmm0, zmmword ptr [r10 + 4*rax]
vmovupd zmmword ptr [rcx + 4*rax], zmm0
jne     .LBB1_19
test    r9, r9
je      .LBB1_22


Here we see the OOP loop making use of the 256-bit ymm registers for the position tuple and velocity tuple, and another for the velocity tuple and acceleration tuple. This is possible because they are adjacent in memory (due to the ordering of the fields). In the DOP loop, the 512-bit zmm register is used.

It seems the performance differences comes from the bandwidth between cache levels, since the performance is identical for the small examples. This can be demonstrated further by removing the extra fields from the struct - in this case we see only a 25% performance difference (godbolt link), and this corresponds to Player struct now being 384 bits (and so 1/4 of the 512-bit read/write is unused).

This emphasises how important it is to consider your deployment target, and if deploying performance-sensitive code, to consider setting the target-cpu explicitly to benefit from all of its features.

It also demonstrates how the ordering of fields can be important to performance. By default Rust will re-order fields automatically, but you can set #[repr(C)] to disable this (necessary for C interoperability for example).

### Summary

This example demonstrates the importance of considering memory layout when aiming for performant code and auto-vectorisation.

Note that the same logic can also apply when working with arrays of structs - making your struct smaller will allow you to load more elements on the same cache line and possibly lead to autovectorisation. Here is an example of a crate (which was shared on the Rust subreddit) that achieved a 40% performance improvement by doing just that.

This particular re-organisation has a direct analogue in database design. A major difference between databases aimed at transactional (OLTP) workloads and analytical (OLAP) workloads is that the latter tend to use columnar-based storage. Just like the case above, this means that operations on one column can take advantage of the contiguous storage and use vector operations, which tends to be the main access pattern for analytical workloads (e.g. calculate the average purchase size across all rows, rather than updating and retrieving entire, specific rows).

In the case of analytical databases this is actually a double win, since it also applies to the serialisation of the data to disk, where compression can now be applied along the column (where the data is guaranteed to be of the same type) leading to much better compression ratios.

If you are working on a problem that might benefit from the struct of arrays approach, and want to run a quick benchmark, you might be interested in the soa-derive crate that will allow you to derive the struct of arrays from your struct.

## Branching in a hot loop

Another optimisation tactic is to avoid branching in any “hot” parts of the code (i.e. any part that will be executed many, many times).

Branching can arise in subtle ways, often by trying to use one struct for many different cases. For example, we might define some general Node type as follows:

enum NodeType {
Player,
PhysicsObject,
Script,
}

struct Node {
node_type: NodeType,
position: (f64, f64),
// ...
}


And then pattern match on node_type when we need to operate on a Node. The problem comes when we have a Vec<Node> with tens of thousands of elements, which we might need to operate on every frame. By using node.node_type to decide the logic to use, we need to check that for every element (since the order of the node_type’s within the Vec<Node> is unknown).

Not only does this comparison mean we must do an extra operation for every element, but it also impedes auto-vectorisation, since our relevant nodes (of the same node_type) may not be stored contiguously.

The data-oriented approach is to split these nodes up by node_type. Ideally creating a separate struct per node type, or at least separating them in separate Vectors before the hot loop. This means we don’t need to check the node_type inside the hot loop, and we can take advantage of the fact that the nodes we do operate on will be stored in contiguous memory.

### Benchmark

In this benchmark we use the following code:

#[derive(Copy, Clone)]
pub struct Foo {
x: i32,
calc_type: CalcType,
}

#[derive(Copy, Clone)]
pub enum CalcType {
Identity,
Square,
Cube,
}

// ...

pub fn run_mixed(x: &[Foo]) -> Vec<i32> {
x.into_iter()
.map(|x| match x.calc_type {
CalcType::Identity => x.x,
CalcType::Square => x.x * x.x,
CalcType::Cube => x.x * x.x * x.x,
})
.collect()
}

pub fn run_separate(x: &[Foo], y: &[Foo], z: &[Foo]) -> (Vec<i32>, Vec<i32>, Vec<i32>) {
let x = x.into_iter().map(|x| x.x).collect();
let y = y.into_iter().map(|x| x.x * x.x).collect();
let z = z.into_iter().map(|x| x.x * x.x * x.x).collect();
(x, y, z)
}


Comparing the case of a mixed vector of Foos, and separate vector of Foos split by calc_type.

The results of the benchmark are as follows:

Overall, we see that the data-oriented approach finishes in about a quarter of the time.

The output on Godbolt is less clear this time, but we can see that there seems to be some unrolling in the separate case that isn’t possible in the mixed case due to the need to check the calc_type in that case.

### Summary

The concept of moving any instructions you can outside of hot loops should be familiar, but this example also demonstrates how it can impact vectorisation.

## Indirection: Iteration in a Linked List

In this example we will compare iterating through a (doubly) linked list vs. a vector. This case is well-known and mentioned in Rust’s LinkedList docs, in Rust’s std::collections docs and in Learn Rust With Entirely Too Many Linked Lists’ Public Service Announcement. The latter Public Service Announcement covers a lot of cases where Linked Lists are commonly used, so I recommend reading that if you haven’t already. Nevertheless, the proof is in the pudding, so I think it’s useful to see a benchmark directly.

A Linked List stores elements indirectly, that is, it stores an element and a pointer to the next element. This means that consecutive elements in the linked list are not stored in consecutive memory locations.

This leads to two issues that impede vectorisation:

• The elements of the linked list may be stored arbitrarily far apart, so we cannot just load a block of memory to the CPU cache to operate on them simultaneously.
• We have to dereference a pointer to get the next element in the list.

For example, we might store a vector of i32s on the heap as follows (holding a pointer to the start of the vector, the vector capacity and the vector length, on the stack):

0x00 1
0x01 2
0x02 3
...


The values are stored contiguously, whereas for a (singly) linked list, we could have the following case.

0x00 1
0x01 0x14
0x0C 3
0x0D 0
...
0x14 2
0x15 0x0C


Here the values are not stored in contiguous memory (or even necessarily in the same order as their pointers maintain in the list).

### Benchmark

In this case the benchmark is very simple, simply squaring all of the elements of a linked list and a vector:

pub fn run_list(list: &mut LinkedList<i32>) {
list.iter_mut().for_each(|x| {
*x = *x * *x;
});
}

pub fn run_vec(list: &mut Vec<i32>) {
list.iter_mut().for_each(|x| {
*x = *x * *x;
});
}


The results are as follows:

Overall, we see that the data-oriented approach finishes in about a tenth of the time.

The output on Godbolt shows the unrolling in the Vec case that isn’t possible in the LinkedList case.

## Summary

This case is well-known and demonstrates the biggest difference of all of the benchmarks. Note that here we look only at iteration, and not at other operations which could be considered to somewhat favour Linked Lists such as insertion, where it avoids the (amortised) cost of vector resizing, however as argued in Learn Rust With Entirely Too Many Linked Lists this can be avoided in Vectors too.

Hopefully this will become common knowledge and we’ll see fewer interview questions and practice problems based around linked lists and indirection, considering only Big O complexity and not real world performance.

## Indirection: Dynamic Dispatch vs. Monomorphisation

When writing generic functions (i.e. for any types implementing certain Traits), we have the choice between dynamic dispatch and monomorphisation.

Dynamic dispatch allows us to work with a mixed collection of trait objects, i.e. we can have a Vec<Box<dyn MyTrait>> which can contain references to different types which all implement MyTrait. The trait object contains a pointer to the instance of the struct itself (implementing MyTrait) and a pointer to the struct’s virtual method table (or vtable, a lookup table pointing to the implementation of each method of MyTrait). Then when we call a method on one of these trait objects, at runtime we work out which implementation of the method to use by consulting the vtable.

Note that this implies indirection. Our vector has to be of pointers to the struct instances themselves (since the different structs implementing MyTrait can differ in size and fields), and we must also dereference the pointer in the vtable to find out which implementation to call.

Monomorphisation, on the other hand, creates a separate implementation of the generic function for each possible type. For example, the following code would actually create two separate functions for run_vecs_square() for the Foo and Bar types respectively:

pub struct Foo {
id: usize,
}

pub struct Bar {
id: usize,
}

pub trait MyTrait {
fn square_id(&mut self);
}

impl MyTrait for Foo {
fn square_id(&mut self) {
self.id = self.id * self.id;
}
}

impl MyTrait for Bar {
fn square_id(&mut self) {
self.id = self.id * self.id * self.id;
}
}

pub fn run_vecs_square<T: MyTrait>(x: &mut Vec<T>) {
x.iter_mut().for_each(|x| x.square_id())
}


This increases the binary size, but gives us an easy way of generating multiple implementations of a function for different types and allows us to avoid indirection (i.e. note we can use Vec<T> and don’t need to use Vec<Box<T>>).

Whereas in the following code, we use dynamic dispatch. There is only one implementation of run_dyn_square but exactly which implementation of square_id() it should call is determined at runtime by consulting the trait object’s vtable.

pub fn run_dyn_square(x: &mut Vec<Box<dyn MyTrait>>) {
x.iter_mut().for_each(|x| x.square_id())
}


This can be more convenient, as we can create the vector containing references to different types with no worry about what the actual types are (only that they implement MyTrait), and we don’t inflate the binary size. However, we are forced to use indirection, since the underlying types could have different sizes, and as we saw with the LinkedList example this can have significant implications for auto-vectorisation.

### Benchmark

Using the example above, the benchmark results are as follows:

Overall, we see that the monomorphised example finishes in about a quarter of the time of the dynamic dispatch one. The monomorphised case with indirection (Vec<Box<T>>) is only slightly faster than the dynamic dispatch case which implies that most of the performance gap is due to the added indirection impeding vectorisation, whereas the vtable lookup itself only adds a small constant overhead.

Unfortunately, in this case Godbolt doesn’t include the target functions in the generated output.

### Summary

This benchmark showed that the main cost of dynamic dispatch is in impeding vectorisation due to the necessary introduction of indirection, and that the cost of the lookup in the vtable itself was relatively small.

This means that you should definitely consider designing for monomorphisation if your methods are doing operations that would greatly benefit from vectorisation (such as the mathematical operations above). On the other hand, if they are carrying out operations that are not vectorised (for example, constructing Strings) then dynamic dispatch may have a negligible cost overall.

As always, benchmark your specific use cases and access patterns when comparing different possible implementations.

## Conclusion

In this article we have seen four cases where considering data layout in memory, and the realities and limitations of the CPU cache has lead to significant performance improvements.

This only scratches the surface of data-oriented design and optimisation. For example, structure packing, padding and alignment was not covered. The data-oriented design book by Richard Fabian also covers some additional topics.

It is important to note that in all of our examples, we did not modify the algorithms we used. All implementations for each case have the same Big O complexity, and yet in practice the performance can vary greatly, with speedups from 2x-10x available just by optimising for vectorisation and other features of modern CPUs.

In summary:

• Favour data structures with less/no indirection and contiguous memory (you’ll also have an easier time with the borrow checker!).
• Avoid branching inside loops.
• Benchmark your use cases and access patterns.
• If deploying performance sensitive code, consider targeting the CPU features of your destination machine (i.e. use RUSTFLAGS).
• Criterion is a great benchmarking tool.
• cargo-asm and Godbolt can be used to inspect the generated assembly (and LLVM intermediate representation).
• perf and flamegraph can be used for performance profiling.

● ● Rust