m10-performance

Benchmarking Rust performance accurately requires using `cargo bench` and the `criterion` crate, which provide statistically rigorous measurements of execution time, helping you detect regressions and validate that your optimization strategies actually improve speed.

The best strategies for optimizing Rust performance include reducing memory allocations by pre-allocating `Vec` capacity, improving cache locality, parallelizing execution with `rayon`, and selecting appropriate data structures to minimize computational overhead.

Improving cache locality speeds up code execution by ensuring data is stored contiguously in memory, which allows the CPU to fetch data faster and reduces cache misses, making memory access patterns significantly more efficient during computation.

Yes, you can use `rayon` for parallelizing execution in your Rust application. It provides data parallelism for collections, allowing you to easily convert sequential iterator computations into concurrent tasks to utilize multiple CPU cores effectively.

Pre-allocating `Vec` capacity reduces memory allocations by reserving the exact required memory upfront, preventing the vector from repeatedly reallocating and copying elements as it grows dynamically during runtime execution.