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Single instruction, multiple data (SIMD) is a stream technology in the computer field. It is different from the previous instruction that can process operands only one by one, but can process multiple operands simultaneously to realize the synchronous processing of calculation instructions. SIMD can also diversify to increase throughput, thereby saving a lot of time lost in the registration process. Computers with SIMD characteristics also realize parallel processing in data processing and operations, and they solve complex computer problems efficiently. SIMD computers are also suitable for solving tasks that require a large number of matrix operations.
However, the theoretical knowledge framework of SIMD computers is not mature enough, and its construction on the physical machine and mathematical models still has a defect in that it is only theoretical and lacks practical evidence. Therefore, according to the parallel operation characteristics of SIMD computers, in this paper we first explain how to construct a physical machine model with a more rational structure. On this basis, the state transition in the model is analyzed comprehensively, and an advanced SIMD computer mathematical model is constructed. Moreover, to improve the scientificity of applying the mathematical model to the actual operation process, this paper shows how to improve the key performances, such as image processing and detection accuracy for data-parallel image processing algorithms.
This paper discusses the following innovations:
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The physical machine model is rationally designed before the construction of the SIMD computer mathematical model, thus laying a solid theoretical foundation for the construction of the mathematical model.
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The convolutional neural network algorithm is introduced to optimize the data-parallel image processing algorithm, reducing the experimental sample error and improving the data classification accuracy.