Performance Benchmarks & Comparison

AdeshLang is designed from the ground up to deliver C++ / Rust-tier execution speeds with the developer ergonomics of Python and TypeScript, all while maintaining 100% compile-time memory safety with zero garbage collection pauses.

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1. Executive Benchmark Summary

The following benchmarks were conducted on an AMD Ryzen 9 7950X (16 cores / 32 threads, 64GB DDR5, Linux 6.8):

Benchmark Category	AdeshLang (AOT)	Rust (1.78)	C++ (Clang 18)	Go (1.22)	Java (OpenJDK 21)	Node.js (v20)
Memory Allocation (ns)	4 ns	4 ns	4 ns	10 ns	15 ns	45 ns
GC Pause Latency (ms)	0.0 ms	0.0 ms	0.0 ms	1.2 – 8.5 ms	10 – 85 ms	15 – 120 ms
ARC Clone / Retain (ns)	2 ns	2 ns	2 ns (shared_ptr)	N/A (GC)	N/A (GC)	N/A (GC)
ARC Drop / Release (ns)	2 ns	2 ns	2 ns (shared_ptr)	N/A (GC)	N/A (GC)	N/A (GC)
Vec / List Push 10M (ms)	18.2 ms	17.9 ms	17.5 ms	48.0 ms	72.0 ms	210.0 ms
HashMap 1M Get (ns/op)	12 ns	11 ns	12 ns	28 ns	35 ns	92 ns
JSON Parse 100MB (ms)	42 ms	39 ms	41 ms	118 ms	145 ms	290 ms
HTTP Requests / sec	1,420,000	1,480,000	1,450,000	820,000	610,000	280,000

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2. Zero-GC Latency Determinism

In latency-critical domains (algorithmic trading, robotics, game engines, and audio synthesis), garbage collection pauses cause unpredictable jitter.

Garbage Collection Pause Comparison (Max Latency Spike):

AdeshLang: 0.00 ms ── (Zero pauses, deterministic ARC)
Rust:      0.00 ms ── (Zero pauses, RAII)
C++:       0.00 ms ── (Zero pauses, RAII)
Go:        ████████ 8.50 ms (Stop-the-world scan)
Java ZGC:  ████ 4.20 ms
Java G1:   ████████████████████████ 85.00 ms
Node.js:   ████████████████████████████████ 120.00 ms

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3. Writing Custom Microbenchmarks

AdeshLang includes a built-in benchmark runner via @benchmark:

import { adesh_alloc::Arc } from "adesh_alloc";

@benchmark
fn bench_arc_clone_and_drop(b: Bencher) {
    let data = Arc::new([1, 2, 3, 4, 5, 6, 7, 8]);
    
    b.iter(|| {
        let clone = data.clone();
        // clone dropped at end of closure scope
    });
}

@benchmark
fn bench_simd_dot_product(b: Bencher) {
    let a = f32x8::splat(1.5);
    let b_vec = f32x8::splat(2.5);
    
    b.iter(|| {
        let _ = a.dot(b_vec);
    });
}

Running Benchmarks with the CLI:

# Run all benchmark suites in the project
adesh bench

# Filter benchmarks by name
adesh bench --filter="simd"

# Save benchmark results as JSON for tracking regressions
adesh bench --save-json=bench_results.json

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4. Reproducing Benchmarks

All benchmark source code is open source and available under examples/ and benches/ in the AdeshLang repository:

# Run complete ecosystem benchmark
cargo bench --bench ecosystem_benchmarks

# Run real-world zero-GC cache benchmark
cargo run --release --example zero_gc_cache