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A structured parallel periodic Arnoldi shooting algorithm for RF-PSS analysis based on GPU platforms

机译:基于GPU平台的结构化并行周期性Arnoldi射击算法用于RF-PSS分析

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The recent multi/many-core CPUs or GPUs have provided an ideal parallel computing platform to accelerate the time-consuming analysis of radio-frequency/millimeter-wave (RF/MM) integrated circuit (IC). This paper develops a structured shooting algorithm that can fully take advantage of parallelism in periodic steady state (PSS) analysis. Utilizing periodic structure of the state matrix of RF/MM-IC simulation, a cyclic-block-structured shooting-Newton method has been parallelized and mapped onto recent GPU platforms. We first present the formulation of the parallel cyclic-block-structured shooting-Newton algorithm, called periodic Arnoldi shooting method. Then we will present its parallel implementation details on GPU. Results from several industrial examples show that the structured parallel shooting-Newton method on Tesla''s GPU can lead to speedups of more than 20× compared to the state-of-the-art implicit GMRES methods under the same accuracy on the CPU.
机译:最近的多核/多核CPU或GPU提供了理想的并行计算平台,以加速对射频/毫米波(RF / MM)集成电路(IC)进行的耗时分析。本文开发了一种结构化的射击算法,该算法可以在周期性稳态(PSS)分析中充分利用并行性。利用RF / MM-IC仿真的状态矩阵的周期性结构,将循环块结构的射击Newton方法并行化并映射到最新的GPU平台上。我们首先提出并行循环块结构射击-牛顿算法的公式,称为周期性Arnoldi射击方法。然后,我们将在GPU上展示其并行实现细节。来自几个工业示例的结果表明,与最新的隐式GMRES方法相比,在CPU上具有相同精度的情况下,特斯拉GPU上的结构化并行射击-牛顿方法可以使速度提高20倍以上。

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