Parallel Framework for Earthquake Induced Response Computation of the Sdof Structure

Abstract

Abstract. Parallel computing briskly diminishes computation time through simultaneous use of multiple computing resources. In this research, parallel computing techniques have been developed to parallelize a program for obtaining a response of single degree of freedom (SDOF) structure under earthquake loading. The study uses Distributed Memory Processors (DMP) hardware architecture and Message Passing Interface (MPI) compilers directives to parallelize the program. The program is made parallel by domain decomposition. Concurrency in the program is created by dividing the program into two parts to run on different computers, calculating forced response and free response of the first half and the second half. Parallel framework successfully creates concurrency and finds structural responses in significant lesser time than sequential programs.

GA‐Based Multi‐Objective Optimization for Retrofit Design on a Multi‐Core PC Cluster

“…Nature‐based structural optimization approaches for large‐scale structures have been developed in recent years (Aldwaik and Adeli, ). In particular, parallel computing for structural optimization have been attempted to reduce the computational burden that accompanies structural analysis (Adeli and Kamal, ; Adeli and Kumar, , b; Munir et al., ). However, considering the problem size for structural analysis and cost of computational resources, the use of a supercomputer may not be economical, or engineers may have difficulty accessing it.…”

“…As some examples, workstation clusters have been used for finite element analysis (Adeli and Kumar, ; Suzuki and Roux, ) and structural optimization (Adeli and Kumar, ; Umesha et al., ). Many cases of clustering multiple inexpensive PCs for structural optimization are increasingly being reported (Park et al., ; Munir et al., ).…”

Fuzzy B-spline algorithm for 3-D lineament reconstruction

“…In referring to Katsuaki et al (1995), Moore et al (1998) and Walsh and Clark (2000), the lienament information extractions in satellite images can be divided broadly into three categories: (i) Lineament enhancement and lineament extraction for characterization of geologic structure;(ii) the image classification to perform geologic mapping or to locate spectrally anomalous zones attributable to mineralization (Mostafa and Quay, 1995;Süzen and Toprak, 1998;Islam et al, 2011a, b, c) and (iii) the superposition of satellite images and multiple data such as geological, geochemical, and geophysical data in a geographical information system (Novak and Soulakellis, 2000;Semere and Ghebreab, 2006;Islam et al, 2012a, b, c;Munir et al, 2012). Furthermore, remote sensing data assimilation in real time could be a bulk tool for geological feature extraction, mapping and structural reconstruction (Islam et al, 2012d, e, f;Jameel et al, 2012a, b, c).…”