CPU Cache-Friendly Data Structures in Go: 10x Speed with Same Algorithm
a day ago
- #cpu-cache
- #go-programming
- #performance-optimization
- Cache misses can slow down code by 60x compared to L1 cache hits.
- False sharing occurs when multiple cores update different variables in the same cache line, causing performance degradation.
- Proper data structure padding can improve performance by 5-10x in specific scenarios.
- Data-oriented design (SoA) is more cache-friendly than object-oriented design (AoS) for high-performance systems.
- Prefetching helps the CPU predict memory access patterns, improving performance for linear access.
- Hot/cold data splitting reduces cache thrashing by separating frequently accessed data from rarely accessed data.
- NUMA-aware allocation improves performance by ensuring data locality for specific CPU cores.
- Branch prediction-friendly code can be optimized by sorting data or using branchless arithmetic.
- Cache-conscious hash tables (e.g., Robin Hood hashing) reduce cache misses and improve lookup speeds.
- SIMD-friendly layouts and aligned memory access enable vectorized processing for performance gains.