This directory contains detailed analysis of applying Rust patterns to high-level languages.
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Allocation Patterns - 2.4x performance improvement
- Buffer reuse eliminates GC pressure
- In-place operations avoid temporaries
- Single-pass algorithms reduce iterations
-
Cache Locality - 38% performance improvement
- Column-oriented storage beats objects
- CPU cache effects matter in all languages
- Memory layout impacts even GC languages
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Ownership Overhead - 66% overhead penalty
- Runtime ownership tracking hurts performance
- GC already provides memory safety
- Ownership patterns only help API design
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Concurrency Overhead - 4-6x slower than single-threaded!
- Isolate communication dominates performance
- Parallelization often hurts for <1MB data
- Worker pools only reduce overhead by 33%
-
Zero-Copy Patterns - Up to 15x performance gains!
- Type punning provides 15x speedup
- View slicing is 7x faster
- Object pooling eliminates GC pressure
-
Async Patterns - Up to 14x faster async operations!
- Microtask scheduling is 14x faster
- Buffered streams provide 5x speedup
- Lazy futures are 1.5x faster
| Optimization | Performance Gain | Complexity | When to Use |
|---|---|---|---|
| Type punning | 15x | Low | Binary data parsing |
| Microtask scheduling | 14x | Low | CPU-bound async work |
| View slicing | 7x | Low | Array segments |
| Buffered streams | 5x | Low | Stream processing |
| StringBuffer | 3.7x | Low | String building |
| Batched concurrency | 3.2x | Medium | Parallel operations |
| Object pooling | 2.7x | Medium | Temporary objects |
| Buffer reuse | 2.4x | Low | Hot paths |
| Lazy futures | 1.57x | Low | Avoiding scheduling |
| Cache locality | 1.38x | Medium | Large datasets |
| Arena allocation | 1.21x | Medium | Many small allocs |
| Ownership patterns | 0.66x (slower) | High | Never for performance |
| Isolate parallelism | 0.25x (4x slower!) | High | Only for >1MB data |
Allocation discipline > Ownership rules
Focus on:
- Reducing allocations
- Improving cache usage
- Processing in-place
Avoid:
- Runtime ownership tracking
- Unnecessary copying
- Complex ownership models
For best understanding:
- Start with Allocation Patterns - biggest impact
- Then Cache Locality - hardware fundamentals
- Finally Ownership Overhead - what not to do
All findings based on benchmarks in /benchmarks/
- Platform: Darwin 25.0.0
- Test size: 1,000,000 iterations
- List size: 1,000 elements
Run yourself: dart benchmarks/01_allocation_patterns.dart