Zero-cost abstractions

written by Ruud van Asseldonk

If you read my blog, you can probably guess that I like Rust. I like Rust for a variety of reasons. Its error model, for example. But today I want to highlight a different reason: zero-cost abstractions.

Recently I had the opportunity to do some optimisation work on Claxon, my FLAC decoder. In the process I found a piece of code which in my opinion demonstrates the power of Rust very well, and I would like to share that with you here:

for i in 12..buffer.len() {
    let prediction = coefficients.iter()
                                 .zip(&buffer[i - 12..i])
                                 .map(|(&c, &s)| c * s as i64)
                                 .sum::<i64>() >> qlp_shift;
    let delta = buffer[i];
    buffer[i] = prediction as i32 + delta;

For context, the following variables are in scope:

buffer: &mut [i32];      // A mutable array slice.
coefficients: [i64; 12]; // A fixed-size array of 12 elements.
qlp_shift: i16;

The snippet is part of a function that restores sample values from residues. This is something that happens a lot during decoding, and this particular loop makes up roughly 20% of the total decoding time. It had better be efficient.


The snippet computes a fixed-point arithmetic inner product between the coefficients and a window that slides over the buffer. This value is the prediction for a sample. After adding the residue to the prediction, the window is advanced. An inner product can be neatly expressed by zipping the coefficients with the window, mapping multiplication over the pairs, and then taking the sum. I would call this an abstraction: it moves the emphasis away from how the value is computed, focusing on a declarative specification instead.

The snippet is short and clear — in my opinion — but there is quite a bit going on behind the scenes. Let’s break it down.

It appears that these high-level constructs come at a price. Many intermediate structures are created which would not be present in a hand-written inner product. Fortunately these structures are not allocated on the heap, as they would likely be in a language like Java or Python. Iterators also introduce extra control flow: zip will terminate after one of its inner iterators is exhausted, so in principle it has to branch twice on every iteration. And of course iterating itself involves a call to next() on every iteration. Are we trading performance for convenience here?

Generated code

The only proper way to reason about the cost of these abstractions is to inspect the generated machine code. Claxon comes with a decode example program, which I compiled in release mode with Rustc 1.13 stable. Let’s take a look at the result:

movslq %r14d,%r11
movslq -0x2c(%r8,%rdi,4),%rsi
imul   %r10,%rsi
movslq -0x30(%r8,%rdi,4),%r14
imul   %rbp,%r14
add    %rsi,%r14
movslq -0x28(%r8,%rdi,4),%rsi
imul   %rdx,%rsi
add    %rsi,%r14
movslq -0x24(%r8,%rdi,4),%rsi
imul   %rax,%rsi
add    %rsi,%r14
movslq -0x20(%r8,%rdi,4),%rsi
imul   %rbx,%rsi
add    %rsi,%r14
movslq -0x1c(%r8,%rdi,4),%rsi
imul   %r15,%rsi
add    %rsi,%r14
movslq -0x18(%r8,%rdi,4),%rsi
imul   %r13,%rsi
add    %rsi,%r14
movslq -0x14(%r8,%rdi,4),%rsi
imul   %r12,%rsi
add    %rsi,%r14
movslq -0x10(%r8,%rdi,4),%rsi
imul   0x8(%rsp),%rsi
add    %rsi,%r14
movslq -0xc(%r8,%rdi,4),%rsi
imul   0x18(%rsp),%rsi
add    %rsi,%r14
movslq -0x8(%r8,%rdi,4),%rsi
imul   0x20(%rsp),%rsi
add    %rsi,%r14
imul   0x10(%rsp),%r11
add    %r11,%r14
sar    %cl,%r14
add    (%r8,%rdi,4),%r14d
mov    %r14d,(%r8,%rdi,4)
inc    %rdi
cmp    %r9,%rdi
jb     10c00 <claxon::subframe::predict_lpc::h6c02f07b190820c0+0x2b0>

All overhead is gone completely. What happened here? First of all, no loop is used to compute the inner product. The input slices have a fixed size of 12 elements, and despite the use of iterators, the compiler was able to unroll everything here. The element-wise computations are efficient too. There are 12 movslqs which load a value from the buffer and simultaneously widen it. There are 12 multiplications and 12 additions, 11 for the inner product, and one to add the delta after arithmetic shifting (sar) the sum right. Note that the coefficients are not even loaded inside the loop, they are kept in registers at all times. After the sample has been computed, it is stored simply with a mov. All bounds checks have been elided. The final three instructions handle control flow of the loop. Not a single instruction is redundant here, and I could not have written this better myself.

I don’t want to end this post without at least touching briefly upon vectorisation. It might look like the compiler missed an opportunity for vectorisation here, but I do not think that this is the case. For various reasons the above snippet is not as obvious to vectorise as it might seem at first sight. But in any case, a missed opportunity for vectorisation is just that: a missed opportunity. It is not abstraction overhead.


In this post I’ve shown a small snippet of code that uses high-level constructs such as closures and iterator combinators, yet the code compiles down to the same instructions that a hand-written C program would compile to. Rust lives up to its promise: abstractions are truly zero-cost.

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