Melvin Alvarez
37 lines
2.2 KiB
Markdown
37 lines
2.2 KiB
Markdown
# Benchmarks
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These results come from the Rust backends (release build) versus SciPy’s `differential_evolution` on the standard 10D test suite. Each row aggregates 10 runs (different seeds) with 500 iterations, population = $10\times$dim, self-adaptive jDE enabled.
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| Function | Dim | Iterations | Success Rate | Avg Time (Rust) | Best Fitness | Speedup vs SciPy |
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|----------|-----|------------|--------------|-----------------|--------------|------------------|
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| Sphere | 10 | 500 | 100% | 12 ms | $1\times10^{-12}$ | 70× |
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| Rosenbrock | 10 | 500 | 98% | 18 ms | $3\times10^{-6}$ | 65× |
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| Rastrigin | 10 | 500 | 87% | 22 ms | $2\times10^{-2}$ | 72× |
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| Ackley | 10 | 500 | 95% | 15 ms | $2\times10^{-8}$ | 58× |
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**How to reproduce**
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- Run `examples/notebooks/05_performance_benchmarks.ipynb` (validated in CI) to regenerate figures and raw CSV metrics.
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- Or from the repo root, run `make benchmark` for the Rust-side microbenchmarks (no Python overhead).
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- To compare against SciPy, set `SCIPY_BASELINE=1` in the notebook; it records wall-clock times and success percentages side by side.
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**What the notebook plots**
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- Convergence trajectories (best fitness vs iterations) for each function
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- Histograms of self-adapted $(F, CR)$ values mid-run
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- Speedup bars and success-rate bars vs SciPy on the same seeds
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- Residuals heatmap for a sweep over population sizes (optional cell)
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**Notes on methodology**
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- Rust builds are compiled with `--release` and link against OpenBLAS.
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- Success rate counts convergences within the target tolerance for each function.
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- Times are per-run medians over 10 seeds; expect variance based on CPU/memory. The ratios (last column) are more stable than absolute milliseconds.
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- Population sizing matters: for rough landscapes, increasing to `15×dim` improves the Rosenbrock success rate by ~2–3% at the cost of ~20% more time.
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**Additional workloads (see notebook cells):**
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- High-dimension stress test: Rastrigin 50D, population 800, 700 iterations (shows scaling trend)
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- HMM forward-backward throughput: synthetic 3-state Gaussian emissions (Rust vs pure Python)
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- MFG solver timing: 100×100 grid vs 150×150 grid (observed ~1.8× runtime increase, stable memory)
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