Practical Application of the Vector-Matrix Model of Computations
Abstract
The paper presents a vector-matrix computational model based on the SIMD (Single Instruction, Multiple Data) architecture, which ensures high efficiency in performing parallel operations on large data arrays. The main focus is on integrating the principles of parallel vector computations into classical computing architectures, significantly increasing performance when solving specialized tasks such as cryptography, big data processing, and optimization problems, particularly the multiple knapsack problem. The vector-matrix model enables the emulation of quantum computing on traditional processors, expanding the applicability of quantum algorithms in classical systems. The paper provides a detailed discussion of methods used to implement matrix multiplication algorithms, cryptographic operations, and scheduling problems, with an emphasis on improving performance and reducing energy consumption. The advantages of the proposed approach in solving real-time tasks and enhancing resilience to side-channel attacks, such as timing analysis, are also discussed.
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