Stridebased

Stridebased describes algorithms and data access patterns that operate on elements spaced by a fixed offset, or stride, in memory. The stride is the distance between consecutive accessed elements, measured in units of elements or bytes. Stridebased access is common when traversing multi-dimensional data laid out in linear memory or when sampling non-contiguous elements for vectorized computation.

In practice, stridebased patterns arise in numerical computing, image processing, and graphics, for example when iterating down a column of a row-major matrix (a non-unit stride) or when processing a tensor with a fixed leading dimension. The choice of stride is influenced by the memory layout, such as row-major versus column-major, and by hardware features like cache lines and prefetchers.

Performance depends on locality. Small strides that align with cache lines preserve spatial locality and reduce cache misses, while large strides can cause scattered access and poor cache utilization. Stride-based code can benefit from loop tiling, reorganization to SoA (structure of arrays) layouts, or explicit vectorization to exploit SIMD units. Prefetching behavior is also affected by regular stride, enabling hardware prefetchers to fetch future elements ahead of use.

Related topics include array layouts, structure of arrays versus array of structures, memory locality, and cache optimization strategies.