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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

tt

ystemtimeExecutionsystemprocessoronetimeExecutionnS )sparallel()()(

Speedup factor

processors of numberssormultiproce a using timeExecutionprocessor 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 Efficiency1 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 Efficiency1 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

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Core Time(s) Speedup Efficiency1 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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