Contiguous memory and cache locality are fundamental concepts in writing high-performance code, essential for developers aiming to optimize their applications. Contiguous memory refers to data stored in consecutive memory addresses, enhancing CPU efficiency by reducing the time spent on memory access. This is particularly beneficial for data structures like arrays and vectors, which are inherently contiguous, as opposed to linked lists or hash tables, which are non-contiguous.

Modern CPUs are optimized for accessing consecutive memory locations, making contiguous memory an important factor in achieving high performance. Cache locality, on the other hand, refers to the effectiveness of a program in using the CPU cache by accessing data that is 'nearby' in memory. This principle is crucial because accessing data from the cache is significantly faster than retrieving it from the main memory.

The memory hierarchy, which includes CPU registers, different levels of cache, and main RAM, highlights the performance differences, with L1 cache being approximately 100 times faster than RAM. Understanding and leveraging these concepts can lead to substantial performance improvements, as demonstrated by real-world examples such as matrix traversal and the difference between array of structures (AoS) and structure of arrays (SoA). By focusing on spatial and temporal locality, developers can ensure their code is cache-friendly, potentially reducing execution time by 10-50 times.

To achieve this, practical optimization tips include using arrays instead of linked lists, accessing data sequentially, and keeping the working set small to fit within the cache.