Art Installation Design and Algorithm Research Oriented to Heterogeneous Computing Architecture and Particle Swarm Algorithm

Traditional high-performance computers generally use commercial general-purpose processors. When constructing large-scale parallel computing systems, they will face many challenges in system efficiency, power consumption, system maintenance, and cost. Heterogeneous architectures have begun to become the key to constructing supercomputer systems. In order to improve the optimization efficiency of the particle swarm algorithm, based on the simplified particle swarm algorithm, an improved strategy for fusion of population information is proposed. Only using the optimal position of the individual particle and the optimal position of the population to update the particle position makes the algorithm description simpler, construct a random term that depends on the population information to increase the diversity of the population, and design an adaptive control function equation to balance the algorithm's global detection and local detection. This article aims to study the design and algorithm of art installations for heterogeneous computing architecture and particle swarm algorithm. Aiming at the two heterogeneous system components of GPU and FPGA in heterogeneous computing systems to solve the dual heterogeneous problem between various computing components and applications, a task flow model for heterogeneous computing systems is proposed, which adopts heterogeneous systems. The structure focuses on the deterministic and nondeterministic calculation methods of particle swarm algorithm and carries out parallel research algorithms of particle transport data to analyze the design of art installations. The experimental results show that by making full use of the GPU hardware storage structure to improve memory access speed and reduce cache failure, the combination of particle swarm algorithm and heterogeneous computing system enables the calculation of the carrier components to obtain 3 times the acceleration effect; through the energy analysis model, Heterogeneous computing system components can obtain at least 1.5 times performance improvement.