Packed data refers to a method of organizing data where multiple smaller data elements are combined into one contiguous data unit, such as a word or byte, to maximize storage efficiency and improve processing throughput. This data packing is particularly valuable in AI and ML for operations that involve vectorized processing or SIMD (Single Instruction, Multiple Data) instructions, allowing for parallel computation over multiple data points within a single execution cycle. The use of packed data can significantly accelerate performance by reducing memory access operations and leveraging the full capability of hardware architectures, such as GPUs and modern CPUs, which are optimized for vectorized operations.
The concept of packed data saw its first usage in the mid-20th century, but gained significant popularity in AI and computational fields in the 1990s with the advent of vector processing and hardware advancements that necessitated efficient data handling techniques to complement increased computational capabilities.
Key contributions to the development and utilization of packed data have stemmed from computer architects and engineers working on hardware innovation, particularly from companies such as Intel and IBM during their development of SIMD architectures, which helped integrate packed data utilization within mainstream processor designs.