RALLP - Rust for All Programming Languages

Applying Rust's performance patterns to optimize any high-level language

🎯 Mission

Can we achieve Rust-like performance in any high-level language by applying Rust's patterns? This research project systematically explores what transfers across languages, what doesn't, and why. Starting with Dart as our first case study, with plans to expand to Python, JavaScript, and more.

🔬 Current Findings

TL;DR: Rust patterns can make high-level languages up to 200x faster! (Dart results shown)

Key discoveries from 11 comprehensive benchmarks:

• Stack allocators provide 200x speedup ✅
• Type punning (reinterpretation) provides 15x speedup ✅
• Loop unrolling and fusion deliver 7.3x improvement ✅
• Rope data structures: 7x faster insertions ✅
• Branch prediction: 4.2x faster with sorted data ✅
• View-based slicing is 7x faster than sublist ✅
• Buffer reuse eliminates 47% of GC pressure ✅
• Cache-friendly layouts improve performance by 38% ✅
• Ownership semantics add overhead without benefits ❌
• Lock-free patterns often backfire (up to 184x slower) ❌

Full findings → | Extended research →

📊 Benchmark Results

Allocation Patterns

Traditional Dart:          30.30μs per operation
Rust-inspired (buffers):  12.48μs per operation (2.4x faster) ✅
Ownership-style:          20.76μs per operation (1.5x faster)
Immutable functional:     41.73μs per operation (1.4x slower)

Concurrency Patterns

Single-threaded:          61μs per operation (baseline) ✅
Worker pool:             247μs per operation (4x slower) ❌
New isolates:            369μs per operation (6x slower) ❌
Parallel map:           2005μs per operation (27x slower!) ❌

Zero-Copy Patterns

Type punning:           269μs vs 4131μs (15x faster!) ✅
View slicing:           110ns vs 760ns (7x faster) ✅
StringBuffer:           2.98μs vs 11.16μs (3.7x faster) ✅
Object pooling:         7ms vs 19ms (2.7x faster) ✅

Async Patterns

Microtask scheduling:   1.9ms vs 26.5ms (14x faster!) ✅
Buffered streams:       1.8ms vs 8.7ms (5x faster) ✅
Batched concurrency:    562μs vs 1805μs (3.2x faster) ✅
Lazy futures:           0.99μs vs 1.56μs (1.57x faster) ✅

🗂️ Project Structure

rallp/
├── docs/
│   ├── patterns/       # Language-agnostic pattern documentation
│   │   ├── allocation.md      # Memory allocation strategies
│   │   ├── zero_copy.md       # Zero-copy techniques
│   │   ├── cache_locality.md  # Data layout optimization
│   │   ├── async.md           # Async/await patterns
│   │   ├── concurrency.md     # Parallelism analysis
│   │   └── ownership.md       # Ownership model analysis
│   └── theory/         # Theoretical foundations
├── languages/          # Language-specific implementations
│   ├── dart/          # ✅ Complete
│   │   ├── benchmarks/
│   │   └── README.md
│   ├── python/        # 🔄 In Progress
│   ├── javascript/    # 📋 Planned
│   ├── go/           # 📋 Planned
│   ├── java/         # 📋 Planned
│   ├── csharp/       # 📋 Planned
│   └── swift/        # 📋 Planned
├── benchmarks/        # Standardized benchmark definitions
│   └── suite/         # Cross-language benchmark suite
└── results/          # Performance comparison data

🚀 Quick Start

Choose Your Language

Dart (Complete - 11 Benchmarks)

cd languages/dart
dart benchmarks/01_allocation_patterns.dart
dart benchmarks/02_concurrency_patterns.dart
dart benchmarks/03_zero_copy_patterns.dart
dart benchmarks/04_async_patterns.dart
dart benchmarks/05_memory_pooling_patterns.dart
dart benchmarks/06_string_optimization_patterns.dart
dart benchmarks/07_iterator_patterns.dart
dart benchmarks/08_simd_vectorization.dart
dart benchmarks/09_lock_free_patterns.dart
dart benchmarks/10_branch_prediction.dart
dart benchmarks/11_compiler_hints.dart

Python (Coming Soon)

cd languages/python
python benchmarks/allocation_patterns.py  # TODO

JavaScript (Planned)

cd languages/javascript
node benchmarks/allocation_patterns.js   # TODO

🔍 Research Areas

Completed

• Allocation patterns comparison
• Buffer reuse strategies
• Cache locality impacts
• Ownership overhead analysis
• Concurrency patterns (isolates vs threads)
• Message passing overhead
• Worker pool patterns
• Zero-copy patterns (views, type punning)
• Memory pooling and arena allocation
• Object pooling strategies
• Async patterns (futures, streams, scheduling)
• Microtask vs event queue analysis
• Stream buffering and backpressure

In Progress

• Real-world application benchmarks
• Cross-language comparisons (Python, JS)
• FFI integration patterns

Planned

• SIMD-like optimizations
• Compiler optimization hints
• Profile-guided optimization
• Memory pooling patterns

💡 Key Insights

1. Allocation Awareness > Ownership Rules

   • Focus on reducing allocations, not tracking ownership
   • Pre-allocate buffers for hot paths
   • Process data in-place when possible

2. Cache Locality Matters Everywhere

   • Even GC languages benefit from cache-friendly layouts
   • Column-oriented storage beats array-of-objects
   • Consider data access patterns

3. Scheduling Is Everything for Async

   • Microtasks are 14x faster than event queue
   • Lazy futures avoid unnecessary scheduling
   • Buffering transforms stream performance

4. Selective Application

   • Apply these patterns in performance-critical code
   • Keep regular code idiomatic and readable
   • Profile first, optimize second

🤝 Contributing

This is an active research project. Ideas and contributions welcome:

1. Add new benchmark scenarios
2. Port benchmarks to other languages
3. Test on different platforms
4. Share real-world results

📚 Resources

• Rust Performance Book
• Dart Performance Best Practices
• Cache-Oblivious Algorithms

📈 Next Steps

1. Expand benchmarks: More real-world scenarios
2. Cross-platform testing: Linux, Windows, ARM
3. Production validation: Apply to actual applications
4. Tool development: Linters for allocation patterns

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Research project exploring how Rust's performance patterns can optimize any programming language