Tilewise

Tilewise describes the practice of performing computations on data that has been partitioned into smaller, adjacent tiles or blocks, rather than operating on the entire data set at once. The approach emphasizes data locality and parallelism by restricting work to subarrays that fit into cache or a processing unit.

Common domains

In image and video processing, tilewise methods process an image in blocks (tiles), enabling parallel processing and better cache usage. In numerical linear algebra, tile algorithms partition matrices into tiles to improve cache reuse and to expose parallelism on multicore CPUs and GPUs. In graphics and rendering, tile-based rendering divides the screen or scene into tiles to manage memory bandwidth and leverage GPU tiling.

Key considerations

Tile size is a critical parameter: too small, and overhead dominates; too large, and memory locality benefits diminish. Boundary handling is another concern, particularly for operations like convolution, where overlapping regions or padding may be required to ensure correctness across tile borders. Synchronization and load balancing become relevant in parallel implementations, especially on heterogeneous hardware.

Benefits and challenges

Tilewise processing can improve performance through enhanced cache locality, reduced memory traffic, and scalable parallelism. It also aligns well with hardware architectures that favor block-level operations, such as GPUs and many-core CPUs. Challenges include selecting optimal tile sizes for a given algorithm and hardware, managing boundaries between tiles, and minimizing coordination overhead.

See also tiling, blocked algorithms, tile-based rendering, and tile-based processing.