axiom-swift-performance

To optimize Swift code, profile with Instruments first to measure bottlenecks, then apply targeted strategies like reducing ARC overhead, leveraging Copy-on-Write, and adjusting memory layout to improve runtime efficiency.

Reduce ARC overhead in Swift by choosing value semantics over reference semantics, utilizing noncopyable types, and optimizing memory layout to minimize unnecessary retain and release cycles.

Copy-on-Write in Swift collections defers data duplication until a mutation occurs, saving memory. Use value semantics with standard collections to trigger this behavior and avoid unnecessary performance penalties.

Use generics specialization when generic code causes runtime inefficiency. Swift can generate type-specific versions of generic functions during compilation, eliminating dynamic dispatch overhead and improving execution speed.

Yes, Swift 6.2 concurrency patterns help resolve performance bottlenecks by structuring asynchronous code efficiently. Profiling identifies slow execution paths, and targeted concurrency features maximize runtime efficiency.

Choosing value vs reference semantics affects Swift performance because value types avoid ARC overhead and enable Copy-on-Write, while reference types require explicit memory management and can introduce retain cycles.