Refactor: optimize gate branching

[yawn-c111]

Related Issue

• Optimize garbling control flow #142

Implementation

Adjusted struct field types to ensure 16-byte memory alignment and C-compatibility on frequently accessed fields.

• crates/mpz-core/src/block.rs
• crates/mpz-garble-core/src/encoding/mod.rs

#[repr(C, align(16))]

pub struct Block([u8; 16]);

Move frequently accessed struct fields outside the loop.

• crates/mpz-garble-core/src/generator.rs
• crates/mpz-garble-core/src/evaluator.rs

let labels = &mut self.labels;
let gates = &mut self.gates;
while let Some(gate) = gates.next() {
    match gate {
        Gate::Xor { x, y, z, } => {
            let x_label = labels[x.id()];
            let y_label = labels[y.id()];
            labels[z.id()] = x_label ^ y_label;

Omitted field name shadowing.

• crates/mpz-garble-core/src/generator.rs
• crates/mpz-garble-core/src/evaluator.rs

Gate::Xor { x, y, z, } => {

Result

Garbling: Approximately 6-9% speed improvement
Evaluation: No significant change

garble/aes128

garble/aes128_batched

garble/aes128_with_hash

evaluate/aes128

[themighty1]

Hi, thank you for your work, I ported this PR to the latest dev and ran some benchmarks. I saw no speed-up in the native build.
Unfortunately, I wasn't able to bench the wasm build, it paniced with ""Uncaught (in promise) RuntimeError: unreachable"" and debugging in the browser is notoriously hard, so I didnt follow up.

I ran our bench harness at https://github.com/tlsnotary/tlsn/tree/main/crates/harness
with the below config before this PR and after and got pretty similar results.

config.toml

[[group]] name = "upload_size" bandwidth = 1000 protocol_latency = 1 download-size = 100

[[bench]]
group = "upload_size"
upload-size = 1000

[[bench]]
group = "upload_size"
upload-size = 2000

[[bench]]
group = "upload_size"
upload-size = 4000

[[bench]]
group = "upload_size"
upload-size = 8000

[[bench]]
group = "upload_size"
upload-size = 10000

[[bench]]
group = "upload_size"
upload-size = 20000

[[bench]]
group = "upload_size"
upload-size = 30000

[[bench]]
group = "upload_size"
upload-size = 40000

before.csv

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after.csv

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