m10-performance

Benchmarking in Rust measures code execution speed and memory usage to pinpoint performance bottlenecks. You use the criterion tool to profile functions, identify slow operations, and establish baselines before applying optimization techniques.

Reduce memory allocations by using pre-allocation, selecting efficient data structures, and implementing Cow<T> to borrow data instead of cloning. These strategies minimize heap usage and improve cache utilization for faster execution.

Profile a slow function by running criterion benchmarks to measure execution time and generating a flamegraph to visualize CPU usage. This identifies exactly which operations consume the most processing time for targeted optimization.

Use parallelization when profiling reveals sequential processing as the bottleneck and the task can be divided across multiple cores. Distributing computational tasks concurrently improves execution speed and overall code efficiency.

Iterator optimization improves Rust application speed by reducing intermediate allocations and enabling compiler optimizations. Profiling identifies where replacing manual loops with iterators maximizes cache utilization and computational efficiency.