Optimization of Clutter Simulation Based on GPU

Abstract

Weibull clutter is used as an example in this paper. Based on the serial parallel analysis of Zero-memory non-linear transformation's Weibull distributed clutter algorithm, fine-grained optimization is performed. The fine-grained part uses the cuBLAS library to optimize the performance of convolution calculations. Compared with CUDA shared memory convolution method and GPU parallel matrix multiplication convolution method, its computational performance can be significantly improved under a large amount of data. Simulation results show that the Zero-memory non-linear transformation's Weibull distributed clutter simulation method is optimized and accelerated. The real-time performance of clutter data is significantly improved and its acceleration effect will be better as the amount of clutter data increases. It turns out that through fine-grained optimization, the performance of convolution calculations with large amounts of data is improved.

Regularized lattice Boltzmann method parallel model on heterogeneous platforms

“…The emergence of multicore processors, 16,17 including central processing units (CPUs) and graphics processing units (GPUs), has opened the door of mainstream computing parallel computing. The GPU first used for graphics calculations and now used for general purpose computation 18,19 . The peak computational performance of GPU is an order of magnitude higher than that of traditional CPU, and GPU has the characteristics of high bandwidth and relatively large dedicated memory 20 .…”

“…The GPU first used for graphics calculations and now used for general purpose computation. 18,19 The peak computational performance of GPU is an order of magnitude higher than that of traditional CPU, and GPU has the characteristics of high bandwidth and relatively large dedicated memory. 20 Smaller, less power consumption, easier to maintain, and inexpensive compared to a CPU cluster, GPUs offer a compelling alternative.…”

Fast Realization of 3-D Space-Time Correlation Sea Clutter of Large-Scale Sea Scene Based on FPGA: From EM Model to Statistical Model

“…The 3-D sea clutter simulation with the statistical model had demonstrated success on the PC platform, but the FPGA implementation only stay at simulation 1-D correlated sequence [15]- [17] and 2-D temporal and spatial correlated clutter sequence [18]. Clutter simulation using the statistical model based on GPU is still at 1-D sequence simulation [19]. The focus of implementation of the 1-D simulation is to change the probability density distribution of the random sequence, and a Gaussian power spectrum is used to change the autocorrelation of the sequence simply.…”