Version: 1.0.0 (All Phases Complete) Status: ✅ Production Ready Quality Standard: EXTREME TDD - Grade A
Complete, production-ready scientific test harness for benchmarking Rust binary optimization techniques. Demonstrates 5-51x performance improvements and 91.7% binary size reductions through systematic optimization with full statistical validation.
- 🚀 51.33x maximum speedup (quicksort with lto-fat)
- ⚡ 15.06x average speedup (lto-fat across all workloads)
- 📦 91.7% binary size reduction (size-ultra profile)
- 📊 Statistical rigor: ANOVA, t-tests, confidence intervals
- ✅ 100% success rate: 150/150 jobs completed, 580 measurements
Figure: Comprehensive optimization results showing 15× average speedup (lto-fat), SIMD acceleration with Trueno (2-7× additional boost), and six proven optimization strategies validated across 10 workloads.
Figure: Statistical analysis showing (A) Pareto frontier for speed vs size tradeoffs, (B) Top profile rankings with confidence intervals, (C) Maximum speedup by workload type, and (D) Performance heatmap across benchmarks and profiles.
This project integrates certeza, trueno, and ruchy-docker best practices for sustainable quality:
# Tier 1: ON-SAVE (sub-second feedback)
make tier1 # Quick checks, unit tests, format - perfect for rapid iteration
# Tier 2: ON-COMMIT (1-5 minutes)
make tier2 # Full tests, coverage, property tests - run before committing
# Tier 3: ON-MERGE (hours)
make tier3 # Mutation testing, exhaustive validation - CI/CD only
# See all options
make helpLearn more: See INTEGRATION.md for comprehensive integration guide.
# Text-based visualizations (no setup required)
python3 text_visualizations.py
# Statistical analysis
cargo run --bin analyze-results
# Quick demo (6 jobs, ~10 seconds)
cargo run --bin pathfinder-demo# Execute all 150 pathfinder jobs (~8 minutes)
cargo run --bin full-pathfinder-execution
# Output: pathfinder_results.json with 580 measurements# One-time setup
python3 -m venv venv
source venv/bin/activate
pip install pandas matplotlib numpy
# Generate publication-ready plots (300 DPI)
python3 generate_visualizations.py- 15.06x average speedup across all workloads
- 53.4% smaller binaries than baseline
- Winner or top-3 for most workload types
| Workload Type | Best Profile | Speedup |
|---|---|---|
| Memory random access | lto-fat | 51.33x |
| CPU iterative | perf-ultra | 25.81x |
| Memory cache-sensitive | opt-s | 22.64x |
| Data structures | standard-release | 11.69x |
| Serialization | lto-fat | 11.88x |
| String processing | perf-ultra | 11.04x |
| CPU recursive | opt-s | 4.32x |
| I/O bound | size-z-native | 1.99x |
- 2.16x speedup (vs baseline)
- 314 KB binary (91.7% size reduction)
- Perfect for embedded/mobile applications
- F-statistic: 19.87 (highly significant, p < 0.001)
- η² = 0.986: Workload type explains 98.6% of variance
- Conclusion: Memory-bound workloads benefit far more than CPU-bound
- Top 5 profiles achieve 13-15x average speedup
- No statistically significant difference overall (overlapping 95% CIs)
- Differences emerge at workload-specific level
- 88.7% stable measurements (CV < 10%)
- CPU-bound benchmarks: 0.3-2% coefficient of variation
- Memory-bound benchmarks: 1-5% CV
- Excellent reproducibility across all non-I/O workloads
compiled-rust-benchmarking/
├── crates/
│ ├── analysis/ # Statistical analysis (32 tests)
│ ├── harness/ # Build, measure, execute (76 tests)
│ └── stats/ # Statistical utilities (8 tests)
├── benchmarks/ # 10 diverse benchmarks (49 tests)
│ ├── ackermann/ # CPU-bound recursive
│ ├── fibonacci/ # CPU-bound recursive
│ ├── prime-sieve/ # CPU-bound iterative
│ ├── matrix-mult/ # Memory-bound cache-sensitive
│ ├── quicksort/ # Memory-bound random access
│ ├── hashmap-ops/ # Data structure operations
│ ├── btreemap-ops/ # Data structure operations
│ ├── string-parse/ # String processing
│ ├── json-parse/ # Serialization
│ └── file-io/ # I/O-bound
├── visualizations/ # Generated plots (PNG, 300 DPI)
├── docs/ # 12 comprehensive documentation files
│ ├── COMPLETE_PROJECT_SUMMARY.md
│ ├── FULL_PATHFINDER_RESULTS.md
│ ├── PHASE_4_SUMMARY.md
│ ├── EXECUTION_GUIDE.md
│ └── VISUALIZATION_GUIDE.md
├── *.csv # Visualization data exports
├── pathfinder_results.json # 580 measurements
└── binary_sizes_fibonacci.json # Binary size data
- pathfinder-demo - Quick validation (6 jobs)
- full-pathfinder-execution - Complete study (150 jobs)
- multi-benchmark-study - Cross-benchmark comparison (30 jobs)
- analyze-results - Statistical analysis
- show-build-matrix - Display 800-job matrix
- generate-configs - Export 80 configurations
- run-pathfinder - Pathfinder overview
- text_visualizations.py - Terminal-based charts (no dependencies)
- generate_visualizations.py - Publication-quality plots
- export_visualization_data.py - CSV export
- analyze_speedups.py - Quick speedup analysis
| Metric | Value | Status |
|---|---|---|
| Total Tests | 165 | ✅ All passing |
| Test Coverage | ~87% | ✅ Exceeds 85% target |
| Clippy Warnings | 0 | ✅ Clean |
| Formatting | 0 violations | ✅ Clean |
| SATD | 0 | ✅ No debt |
| Dead Code | 0% | ✅ Clean |
| TDG Grade | A | ✅ 85+ points |
| Job Success Rate | 100% | ✅ 150/150 |
| Measurement Stability | 88.7% | ✅ CV < 10% |
| Phase | Description | Status | Tests | Achievement |
|---|---|---|---|---|
| 0 | Quality Infrastructure | ✅ | 13 | PMAT + TDG |
| 1 | Benchmark Suite | ✅ | 58 | 10 benchmarks |
| 2 | Build Infrastructure | ✅ | 32 | 800-job matrix |
| 2.5 | Pathfinder | ✅ | 26 | 15 profiles |
| 2.6 | Validation | ✅ | - | 30 tests |
| 3 | Execution | ✅ | - | 51.33x speedup |
| 4 | Statistical Analysis | ✅ | 32 | ANOVA, CIs |
| 5 | Visualizations | ✅ | - | Text + graphical |
All objectives achieved ✅
- README.md - This file
- EXECUTION_GUIDE.md - How to run studies
- VISUALIZATION_GUIDE.md - How to create plots
- FULL_PATHFINDER_RESULTS.md - Complete analysis (420 lines)
- MULTI_BENCHMARK_RESULTS.md - Cross-benchmark insights (285 lines)
- PHASE_4_SUMMARY.md - Statistical analysis (550 lines)
- PHASE_4_DESIGN.md - Statistical analysis design (380 lines)
- PROJECT_STATUS.md - Development history (380 lines)
- COMPLETE_PROJECT_SUMMARY.md - Final comprehensive summary
- FINAL_PROJECT_STATUS.md - Production readiness checklist
Total Documentation: ~3,900 lines across 12 files
This project embodies the Toyota Production System:
✅ Genchi Genbutsu (現地現物) - Go and See
- 580 real measurements collected
- Binary sizes measured, not estimated
- 51.33x max speedup discovered empirically
✅ Muda (無駄) - Waste Elimination
- Pathfinder reduced validation by 81% (150 vs 800 jobs)
- Adaptive iterations saved 22% of execution time
- Completed in 8 minutes (vs estimated 1-3 hours)
✅ Kaizen (改善) - Continuous Improvement
- Iterative optimization: 1x → 15x → 51x
- Statistical rigor ensures improvements are real
- CIs quantify uncertainty for better decisions
✅ Jidoka (自働化) - Built-in Quality
- 165 tests all passing
- EXTREME TDD throughout all phases
- Automatic stability detection (CV < 10%)
✅ Muri (無理) - No Overburden
- Efficient resource usage
- Clear progress indicators and ETAs
- Text visualizations work immediately (no setup)
Apply to your projects:
- Identify your workload type (CPU/Memory/I/O)
- Check recommendations table above
- Apply optimal profile to
Cargo.toml - Measure production impact
Example for memory-bound random access:
[profile.release]
inherits = "release"
opt-level = 3
lto = "fat"
codegen-units = 16Extend the study:
- Run full 800-job matrix (all 80 configurations)
- Implement Bayesian analysis (factor importance)
- Measure compile times
- Add hardware profiling
Data available:
pathfinder_results.json- 580 measurements- 4 CSV files for visualization
- Binary size data for 15 profiles
Benchmark methodology:
- Adaptive iteration (3-10 runs based on stability)
- Structured output parsing
- Statistical significance testing
- Comprehensive documentation
- ruchy: Main Ruchy compiler
- ruchy-lambda: AWS Lambda optimization
- ruchy-docker: Docker runtime benchmarking
If you use this infrastructure in research, please cite:
Rust Binary Optimization Benchmarking Infrastructure (2025)
Scientific test harness demonstrating 5-51x performance improvements
through systematic optimization with statistical validation.
MIT - see LICENSE file.
Contributions welcome! Please ensure:
- All tests pass (
cargo test --workspace) - No clippy warnings (
cargo clippy --workspace --all-targets -- -D warnings) - Code is formatted (
cargo fmt --all) - EXTREME TDD maintained (test-first development)
Built with EXTREME TDD methodology, achieving:
- 165 comprehensive tests
- 87% test coverage
- Grade A quality (TDG)
- Zero warnings, zero debt
- 100% reproducible results
🏆 EXTREME TDD Maintained Throughout All Phases! 🏆
Version: 1.0.0 Status: ✅ Production Ready - All Phases Complete Last Updated: 2025-11-10
For detailed instructions, see COMPLETE_PROJECT_SUMMARY.md