Post on 13-Jan-2016
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Parallel computing technique for EM modeling
makai
天津大学电子信息工程学院School of Electronic Information Engineering
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Contents
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1 、 background
2 、 Basic theory of parallel computing
3 、 An example
4 、 plan
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Background
School of Electronic Information Engineering
Arificial neural network (ANN) techniques have been recognized as a powerful tool in electromagnetic(EM)-based modeling and design optimization of microwave passive components . ANN can learn EM responses versus geometrical variables through an automated training process, and the trained ANN can be used as accurate and fast models in the design optimization.
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Background
School of Electronic Information Engineering
“Efficient design optimization of microwave circuits using parallel computational methods,” European Microwave Conference, Amsterdam, the Netherlands, Nov. 2012.
“Parallel automatic model generation technique for microwave modeling,” in IEEE MTT-S Int. Microw. Symp. Dig., Honolulu, Hawaii, June 2007.
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Basic theory of parallel computing
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OpenMP Programming Model
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Shared Memory Model
Uniform Memory Access Non-Uniform Memory Access
OpenMP is designed for multi processor/core, shared memory machines
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MPI(message passing interface) Programming Model
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distributed memory architecture hybrid distributed shared memory architecture
Distributed Memory Model
MPI was designed for distributed memory architectures. Basic MPI Concepts:A set of processes executing in parallel. These
processes have separate address space.
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Parallel computing environment
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Figure 1 A Windows HPC Server cluster of workstations
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Performance Measurement for Parallel Computing
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p
s
t
t
ystemtimeExecution
systemprocessoronetimeExecutionnS
)sparallel(
)()(
Speedup factor
processors of numberssormultiproce a using timeExecution
processor one using time ExecutionE
Efficiency
%n
S(n)E 100 when E is given as a percentage
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Parallel Algorithm Design process
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Problem
Mapping
Communication
Partitioning
Agglomeration
Figure 2 PCAM process
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An example of parallel computing
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An example of parallel computing
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Problem description
359
0
10000000
1
)sin(k i
kk iLWSum =
Figure 3 Data flow chat
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Comparison of the running result
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Core Time(s) Speedup Efficiency
1 196.724 1.0 100%
2 99.735 1.9 95.0%
3 66.452 2.9 96.7%
4 52.235 3.8 95.0%
5 50.941 3.9 78.0%
6 46.406 4.2 70.0%
8 40.895 4.8 60.0%
Table1 Parallel on one computer
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Comparison of the running result
School of Electronic Information Engineering
Core Time(s) Speedup Efficiency
1 196.724 1.0 100%
2 95.722 2.0 100%
4 48.980 4.0 100%
6 34.680 5.7 95.0%
8 28.468 6.9 86.2%
10 25.409 7.7 77.0%
12 23.053 8.5 70.8%
Table2 Parallel on two computers
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Comparison of the running result
School of Electronic Information Engineering
Core Time(s) Speedup Efficiency
1 196.724 1.0 100%
3 64.221 3.0 100%
6 32.658 6.0 100%
9 22.353 8.8 95.0%
12 18.113 10.9 90.8%
15 16.925 11.6 77.3%
18 15.469 12.7 70.6%
24 13.748 14.3 59.6%
Table3 Parallel on three computers
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www.themegallery.com天津大学电子信息工程学院
School of Electronic Information Engineering