parallel computing technique for em modeling
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天津大学电子信息工程学院 School of Electronic Information Engineering. Parallel computing technique for EM modeling. makai. Contents. 1 、 background. 2 、 Basic theory of parallel computing. 3 、 An example. 4 、 plan. School of Electronic Information Engineering. Background. - PowerPoint PPT PresentationTRANSCRIPT
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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
School of Electronic Information Engineering
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
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“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
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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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School of Electronic Information Engineering