Abstract

We are developing a structural health monitoring system for infrastructures (i.e. bridge). We are going to employ wave numerical analysis for acoustics by FDTD (Finite Difference Time Domain) for analysis. However, FDTD needs huge compute resource and time for large area analysis such as bridges. In order to reduce the compute time, we constructed a 20 nodes computer cluster called SAFHC. In this paper, we reported FDTD parallelization on SAFHC. SAFHC consists of ZYBO and Open Blocks built in ARM processor, is able to execute program with low power consumption. In an evaluation, we achieved 14.8 times faster at 20 notes.

Highlights

  • Most infrastructures, such as office buildings, bridges, high ways, and so on, were built in 1970’s and 1990’s in Japan

  • It consists of ARM+FPGA nodes (4 nodes), ZYBO node (16 nodes), an electric power management system, a data base server for the electric power management and a host computer for the calculations

  • The ARM+FPGA nodes and ZYBO nodes are used for low electric power calculation notes

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Summary

Introduction

Most infrastructures, such as office buildings, bridges, high ways, and so on, were built in 1970’s and 1990’s in Japan. The building structures are checked by hammering test of specialists. In order to check the numerous old infrastructures, we need an automatically hammering test system. In the hammering test system, we analyze hammering acoustics wave on the infrastructures by FDTD (Finite Difference Time Domain). FDTD is applied into simulation of Electro dynamics modeling techniques It transforms Maxwell’s equation into difference equations which are discretized to time domain and space domain. PC clusters require larger and more fans to cool down the CPUs. PC clusters that use low-power processors have been developed[1,2,3,4,5,6]. SAFHC employ dual core ARM which is one of the low power processor and FPGA (ZYBO board). We measure execution time and communication time of FDTD and speedup ratio including the number of the compute nodes

Analysis by FDTD
Absorbing Boundary Condition
Incident Wave
Shared Memory and Distributed memory
Parallelization by Open MP
Data Distribution to The Nodes
Parallelization by MPI
Communication Method for Shared Adjacent Data
Acoustics by FDTD Program
Simulation Result
Related works
Conclusions
Full Text
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