Parallel Differential Evolutionary Particle Filtering Algorithm Based on the CUDA Unfolding Cycle

Kaijie Huang,Jie Cao,Rajesh Kaluri

doi:10.1155/2021/1999154

Kaijie Huang, Jie Cao + Show 1 more

Open Access

https://doi.org/10.1155/2021/1999154

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Abstract

Aiming at the problem of low statute efficiency of prefix sum execution during the execution of the parallel differential evolutionary particle filtering algorithm, a filtering algorithm based on the CUDA unfolding cyclic prefix sum is proposed to remove the thread differentiation and thread idleness existing in the parallel prefix sum by unfolding the cyclic method and unfolding the thread bundle method, optimize the cycle, and improve the prefix sum execution efficiency. By introducing the parallel strategy, the differential evolutionary particle filtering algorithm is implemented in parallel and executed on the GPU side using the improved prefix sum computation during the algorithm update. Through big data analysis, the results show that this parallel differential evolutionary particle filtering algorithm with the improved prefix sum statute can effectively improve differential evolutionary particle filtering for nonlinear system states and real-time performance in heterogeneous parallel processing systems.

Highlights

Particle filtering is a sequential Monte Carlo method that employs particles to approximate the posterior probability density distribution
To address the problem of thread differentiation in the execution of the differential evolutionary particle filtering parallel algorithm, based on CUDA architecture, this paper proposes a differential evolutionary particle filtering algorithm based on unfolding cyclic prefixes and optimization to remove thread differentiation and reduce the lag caused by judgment and branch prediction, which makes the particle filtering algorithm gradually improve the computational performance
We propose a CUDA unfolding loop-based state estimation method for differential evolutionary particle filtering to address the problem of inefficient parallel differential evolutionary particle filtering with parallel execution threads and improve the execution efficiency of the prefix sum by unfolding the prefix sum method with an unfolding loop and a thread bundle

Summary

Introduction

Particle filtering is a sequential Monte Carlo method that employs particles to approximate the posterior probability density distribution. To address the computational complexity problem, literature [3,4,5] proposed a GPU-based particle filtering parallel algorithm, which effectively combines the traditional particle filtering algorithm with GPU to make full use of the performance of GPU parallel computing and accelerate the computational speed of the particle filtering algorithm. Literature [6, 7] proposed a GPU-based parallel optimization design and implementation of particle filtering to improve the computational speed of the tracking algorithm. Literature [8,9,10] designed and implemented a parallel particle swarm optimization algorithm based on CUDA, which uses a large number of GPU threads to accelerate the convergence speed of the whole particle swarm.

Differential Evolutionary Particle Filtering Algorithm

Improved Parallel Prefix Sum

Experiment and Performance Analysis

Findings

Conclusion