arrayprocessing

Array processing refers to techniques for manipulating data organized as arrays, typically vectors, matrices, or higher-dimensional tensors. It encompasses both software methods for writing concise, vectorized code and hardware techniques that exploit data-level parallelism. In modern languages and libraries, array processing enables operations on entire arrays with a single expression, using concepts such as vectorization, broadcasting, slicing, and reductions. Typical operations include elementwise arithmetic, matrix and tensor algebra, linear algebra routines, reductions (sum, min, max), and convolutions. These operations are often implemented through optimized kernels that leverage cache locality and data parallelism, using SIMD instructions on CPUs or GPU pipelines.

In practice, array processing is central to fields ranging from scientific computing to image and signal processing. Applications include fast numerical linear algebra, image filtering, Fourier transforms, and machine learning preprocessing. In signal processing, array processing also refers to handling data from multiple sensors or antennas, enabling beamforming, direction finding, and interference suppression using algorithms such as MVDR, MUSIC, or ESPRIT.

Software ecosystems for array processing include Matlab, NumPy/SciPy in Python, R, Julia, and specialized array programming languages and domain-specific languages such as APL, Q, or dataflow frameworks. Performance depends on memory bandwidth, data layout (row-major versus column-major), alignment, and the ability to exploit vector units. Common challenges include maintaining numerical stability in large reductions, avoiding unnecessary temporaries, and balancing CPU–GPU workloads.