unbrice / bench_vtables

A playground to experiment with various representation of V-Tables for multiple Rust traits. I created it in the context of rust-lang #2035

SieveTable: v-tables for sum traits

This repo is a playground to experiment with various representations of V-Tables for multiple Rust traits.

I this repo in the context of rust-lang #2035. I wrote a blog post presenting the issue as well as a proposed solution. This repo is mostly so that I could benchmark that propsal. It could be useful to benchmark other approaches.

To give an overview, it proposes a representation that:

• Allows turning a sum trait to a subset of the sum (Box<A+…+B+…+C> → Box<A+B+C>) without heap allocations.
• Keeps pointers to a sum-trait type on a regular two words fat-pointer.
• Performs better than the often discussed "extra fat" pointers, at least on my x86_64 machine according to my naive benchmarks.
• Does not require full program optimisation (individual crates can remain compiled separately).
• Offers multiple variants and tuning opportunities for varying architectures.

More details in the blog post. Bencmarking is an art I am far from mastering, contributions are welcome.

Bench results

src/fibonacci.rs implements an inefficient recursive Fibonacci based on virtual calls. benches/fibonacci.rs benches it for various number of traits.

On 2021-10-24, I ran on a 2019 Mac cargo criterion and observed the following:

	Size	1 trait	2 traits	3 traits	4 traits	5 traits
VPtr	2 words	402ns	-	-	-	-
PackedSieve	2 words	-	464 ns	488 ns	482 ns	495 ns
InlineSieve	3 words	-	510 ns	510 ns	510 ns	510 ns
MultiVPtr (Extra-fat pointers)	N+1 words	-	472 ns	753 ns	764 ns	831 ns

Interpretation

• PackedSieve performed as well as MultiVPtr for 2 traits, and better for all other trait numbers, while also being smaller.
• InlineSieve has the advantage of stable performance, as it does not depend on the number of traits. Nevertheless, it is a bit larger and significantly more complex.

License

Licensed under either of

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.