Structural Analysis on Computers

Linear Static Structural and Vibration Analysis on High-Performance Computers

Parallel computers offer the opportunity to significantly reduce the computation time necessary to analyze large-scale aerospace structures. This paper presents algorithms developed for and implemented on a massively-parallel computers hereafter referred to as Scalable High Performance Computers (SHPC) for the most computationally intensive tasks involved in structural analysis, namely, generat...

An Implementation of a Generalized Lanczos Procedure for Structural Dynamic Analysis on Distributed Memory Computers

This paper describes a parallel implementation of a generalized Lanczos procedure for structural dynamic analysis on a distributed memory parallel computer. One major cost of the generalized Lanczos procedure is the factorization of the (shifted) stiffness matrix and the forward and backward solution of triangular systems. In this paper, we discuss load assignment of a sparse matrix and propose...

A Parallel-Vector Algorithm for Rapid Structural Analysis on High-Performance Computers

A fast, accurate Choleski method for the solution of symmetric systems of linear equations is presented. This direct method is based on a variable-band storage scheme and takes advantage of column heights to reduce the number of operations in the Choleski factorization. The method employs parallel computation in the outermost DO-loop and vector computation via the "loop unrolling" technique in ...

Cellular Automata for Structural Optimization on Recongfigurable Computers

Principal Component Analysis on Vector Computers

Principal component analysis is a classical multivariate technique. This is a basic tool in the eld of image processing. Due to the iterative performing and the high computational cost of this algorithms over conventional computers , they are good candidates for the pipeline processing. In this work we analyse this code from the vectorization approach and present an eecient implementation on th...