Obtaining a 35x Speedup in 2D Phase Unwrapping Using Commodity Graphics Processors

P. Karasev, D. Campbell, M. Richards
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引用次数: 21

Abstract

Graphics processing units (GPUs) are a powerful tool for numerical computation. The GPU architecture and computational model are uniquely designed for high-resolution high-speed grid-based calculations. This capability can be utilized to accelerate certain classes of compute-intensive radar signal processing algorithms. Characteristics of a problem well-suited for computation on a GPU include high levels of data parallelism, low control logic, uniform boundary conditions, and well-defined input and output. We describe the implementation of two-dimensional multigrid least-squares weighted phase unwrapping on a GPU and demonstrate a large speedup over C and MATLAB implementations. Details of the GPU computation are provided. Background information on the GPU architecture and its applicability to general-purpose computation is discussed.
使用普通图形处理器在2D阶段展开中获得35倍加速
图形处理单元(gpu)是一种强大的数值计算工具。GPU架构和计算模型专为高分辨率高速网格计算而设计。这种能力可用于加速某些类型的计算密集型雷达信号处理算法。适合在GPU上计算的问题的特征包括高水平的数据并行性、低控制逻辑、统一的边界条件和定义良好的输入和输出。我们描述了二维多网格最小二乘加权相位展开在GPU上的实现,并演示了与C和MATLAB实现相比的大幅加速。提供了GPU计算的细节。讨论了GPU架构的背景信息及其在通用计算中的适用性。
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