Profiling Code in Rust - by Vitaly Bragilevsky - Rust Linz, December 2022

Rust

Learn how to identify and optimize performance issues in Rust code using profiling tools like Valgrind, `cargo run`, and flame graphs, and explore techniques for simplifying data structures, reducing memory allocations, and benchmarking.

Key takeaways

Profiling code in Rust is useful to identify performance issues and optimize program behavior.
Rust profiling tools are available, including Valgrind and cargo run.
Profile data can be visualized using flame graphs.
Memory allocation and deallocation are important areas to profile.
Simplifying data structures, such as using vec! instead of String, can reduce memory usage.
Efficient data structures, such as using Rc (Reference Counting) or Arc (Atomic Reference Counting), can reduce memory allocations.
Reducing the number of allocations can significantly improve program performance.
Pre-mature optimization should be avoided, and focus on understanding the problem and identifying areas for improvement before optimizing.
Profiling and optimization should be done iteratively, with testing and validation at each step.
Docker simplifies the profiling process by allowing you to run your program in a container.
The criterion library can be used to benchmark and profile Rust code.
Valgrind is a powerful tool for memory analysis, but it can be challenging to use.
Instruments is another tool for profiling and analyzing Rust code.
A simple technique for optimizing memory allocation is to resize vectors in advance.
Another technique is to avoid temporary structures and reuse existing data.
Benchmarking and profiling should be done at different levels, from simple metrics like memory usage to more complex analysis like function call frequency.

Profiling Code in Rust - by Vitaly Bragilevsky - Rust Linz, December 2022

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