Vectorprocessing

Vectorprocessing is a form of computing in which operations are performed on vectors—ordered lists of data elements—rather than on single scalar values. The primary goal is to apply the same operation to multiple data elements simultaneously, exploiting data parallelism to improve throughput. This approach is realized in hardware as vector processors or through vectorized instruction sets in general purpose processors, as well as in graphics processing units and accelerators.

Historically, vector processing emerged with dedicated vector supercomputers in the 1960s and 1970s, where specialized vector registers and pipelines enabled significant performance gains for numerical workloads. In contemporary CPUs, SIMD extensions (such as SSE/AVX on x86 and NEON on ARM) provide vector units that operate on wide registers and execute the same operation across many elements. GPUs extend the concept to massive data parallelism with hundreds or thousands of lightweight processing elements.

Software can exploit vector processing via automatic or manual vectorization. Compilers analyze code to generate vector instructions; programmers may use intrinsics or high level APIs (for BLAS, linear algebra libraries) to ensure data alignment and contiguous memory access. Performance depends on vector width, memory bandwidth, loop structure, and algorithm suitability for parallelization.

Applications include linear algebra, signal and image processing, simulation, cryptography, and certain machine learning workloads, where vectorized operations underpin efficiency.

See also: SIMD, vectorization, BLAS, intrinsics, GPU computing, data parallelism.