A GPU-Accelerated Parallel Shooting Algorithm for Analysis of Radio Frequency and Microwave Integrated Circuits


This paper presents a brand new parallel shooting-Newton technique based on a graphic processing unit (GPU)-accelerated periodic Arnoldi shooting solver (GAPAS) for fast periodic steady-state analysis of radio frequency/millimeter-wave integrated circuits. The new algorithm 1st explores a periodic structure of the state matrix by using a periodic Arnoldi algorithm for computing the ensuing structured Krylov subspace in the generalized minimal residual (GMRES) solver. The resulting periodic Arnoldi shooting technique is very amenable for huge parallel computing, like GPUs. Second, the periodic Arnoldi-primarily based GMRES solver within the shooting-Newton method is parallelized on the recent NVIDIA Tesla GPU platforms. We have a tendency to more explore CUDA GPUs features, such as coalesced memory access and overlapping transfers with computation to spice up the efficiency of the ensuing parallel GAPAS methodology. Experimental results from several industrial examples show that compared with the state-of-the-art implicit GMRES methodology below the identical accuracy, the new parallel shooting-Newton method can lead up to $8times$ speedup.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE :GPU-Accelerated High-Throughput Online Stream Data Processing - 2018ABSTRACT:The Single Instruction Multiple Data (SIMD) architecture of Graphic Processing Units (GPUs) makes them perfect for parallel processing
PROJECT TITLE :Cost-Optimal Caching for D2D Networks With User Mobility: Modeling, Analysis, and Computational Approaches - 2018ABSTRACT:Caching well-liked files at the user equipments (UEs) provides an efficient way to alleviate
PROJECT TITLE :Design, Analysis, and Implementation of ARPKI: An Attack-Resilient Public-Key Infrastructure - 2018ABSTRACT:This Transport Layer Security (TLS) Public-Key Infrastructure (PKI) is based on a weakest-link security
PROJECT TITLE : Depth Reconstruction From Sparse Samples: Representation, Algorithm, and Sampling - 2015 ABSTRACT: The fast development of 3D technology and computer vision applications has motivated a thrust of methodologies
PROJECT TITLE : GPU-Accelerated Parallel Sparse LU Factorization Method for Fast Circuit Analysis - 2016 ABSTRACT: Lower higher (LU) factorization for sparse matrices is the foremost necessary computing step for circuit simulation

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry