Always consider the algorithmic complexity and performance implications of data structure choices, memory allocation patterns, and container operations. This includes pre-allocating containers when sizes are known, choosing appropriate data structures based on usage patterns, and measuring performance impact of changes. Key practices: 1. **Pre-allocate containers**: Reserve memory when the final size is predictable to avoid repeated reallocations 2. **Choose containers wisely**: Consider trade-offs between different data structures (e.g., `std::vector` vs `std::unordered_set` vs `std::set`) based on access patterns, insertion frequency, and memory constraints 3. **Size buffers defensively**: When working with external libraries that require "big enough" buffers, add safety margins (e.g., +20%) to prevent overflows 4. **Benchmark performance changes**: Always measure performance impact when modifying algorithms, especially for hot paths Example of good practice: ```cpp // Pre-allocate when size is known structure_granule.all_paths.reserve(structure_granule.num_paths); // Choose appropriate container based on usage // For frequent lookups with few duplicates: std::unordered_set // For ordered iteration: std::set // For simple iteration: std::vector constexpr size_t initial_size_degree = 9; // Conservative default for hash tables ClearableHashSetWithStackMemory, initial_size_degree> set; // Size buffers defensively for external libraries PaddedPODArray compressed_buffer(uncompressed_size * 1.2); // +20% safety margin ``` This approach prevents performance regressions, reduces memory fragmentation, and ensures predictable behavior under different load conditions.