SGI OpenFOAM Cloud Benchmark, OpenFOAM Study Meeting for beginner@Kansai, 7th Jul. 2012

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Jul. 7th 2012 OpenFOAM Study Meeting for beginner @ Kansai Masashi Imano
Masashi Imano
SGI OpenFOAM Cloud
Benchmark
1

Jul. 7th 2012 OpenFOAM Study Meeting for beginner @ Kansai Masashi Imano
Test cluster conditions
• Hardware: SGI Altix ICE8200
• CPU: Intel Xeon X5365 3.00GHz 4cores x 2
• Node: 16 nodes
• Memory: 16GB/node (No swap memory)
• Interconnect: InﬁniBand DDR
• OS: SUSE LINUX ENTERPRISE SERVER 10.3
• MPI:
✓ SGI MPT (1.2.6)
✓ OpenMPI
(ThirdParty package distributed with OpenFOAM)
2

Jul. 7th 2012 OpenFOAM Study Meeting for beginner @ Kansai Masashi Imano
• OpenFOAM versions: 2.1.x
• MPI:
✓ OpenMPI (Ver. 1.5.3, mpirun options: --mca btl openib,self,sm)
✓ SGI MPT (Ver.1.2.6)
• Benchmark problem
✓ Flow: Lid-driven 2D cavity (tutorials)
✓ Re: 100 (laminar)
✓ solver: icoFoam (piso)
✓ Linear solver: AMG (p), PBiCG (U)
✓ Mesh:
S(500 2,Δt:5x10-5 s), M(1000 2, Δt:10-5 s), L(2000 2, Δt:10-5 s)
x
Ux = 1 m/s
d = 0.1 m
y
Calculation conditions
3

Jul. 7th 2012 OpenFOAM Study Meeting for beginner @ Kansai Masashi Imano
Benchmark results
0
37500
75000
112500
150000
1 2 4 8
127000
140000
80000
15940
10810
21700
60000
37700
Timesteps/Day
Number of Nodes
OpenMPI SGI MPT
Mesh: S (5002= 1/4 Millions)
4

Jul. 7th 2012 OpenFOAM Study Meeting for beginner @ Kansai Masashi Imano
Benchmark results
0
2500
5000
7500
10000
1 2 4 8
8470
9240
5520
3260
4620
6240
4940
317
Timesteps/Day
Number of Nodes
OpenMPI SGI MPT
Mesh: M (10002 =1 Million)
80%up
50%up
5

Jul. 7th 2012 OpenFOAM Study Meeting for beginner @ Kansai Masashi Imano
Benchmark results
0
750
1500
2250
3000
1 2 4 8
2520
1560
510
280
1780
1430
450
260
Timesteps/Day
Number of Nodes
OpenMPI SGI MPT
Mesh: L (20002 =4 Millions)
40%up
6

Jul. 7th 2012 OpenFOAM Study Meeting for beginner @ Kansai Masashi Imano
Any Questions?
7

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@misc{Imano202020OCFIS2020slide, author={今野雅}, title={{「OpenFOAMソルバの実行時ベイズ最適化」発表資料}}, booktitle={OpenCAE・FrontISTR合同シンポジウム2020}, organization={オープンCAE学会}, year={2020}, month={Dec}, note={\href{http://www.opencae.or.jp/activity/symposium/ocfis2020/}{http://www.opencae.or.jp/activity/symposium/ocfis2020/}, (accessed 2020-12-09)} }

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Water Industry Process Automation & Control Monthly - April 2024

Water Industry Process Automation & Control Monthly - April 2024

Water Industry Process Automation & Control Monthly - April 2024

Water Industry Process Automation & Control

Online banking management system project.pdf

Online banking management system project.pdf

Online banking management system project.pdf

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Call for Papers - International Journal of Intelligent Systems and Applicatio...

Call for Papers - International Journal of Intelligent Systems and Applicatio...

Call for Papers - International Journal of Intelligent Systems and Applicatio...

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Unleashing the Power of the SORA AI lastest leap

Unleashing the Power of the SORA AI lastest leap

Unleashing the Power of the SORA AI lastest leap

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Intze Overhead Water Tank Design by Working Stress - IS Method.pdf

Intze Overhead Water Tank Design by Working Stress - IS Method.pdf

Intze Overhead Water Tank Design by Working Stress - IS Method.pdf

PVC VS. FIBERGLASS (FRP) GRAVITY SEWER - UNI BELL

PVC VS. FIBERGLASS (FRP) GRAVITY SEWER - UNI BELL

PVC VS. FIBERGLASS (FRP) GRAVITY SEWER - UNI BELL

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University management System project report..pdf

University management System project report..pdf

University management System project report..pdf

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LIST OF EXPERIMENTS: 1. Implement simple vector addition in Tensor Flow. 2. Implement a regression model in Keras. 3. Implement a perception in TensorFlow/Keras Environment. 4. Implement a Feed Forward Network in TensorFlow/Keras. 5. Implement an image classifier using CNN in TensorFlow/Keras. 6. Improve the deep Learning model by fine tuning hyper parameters. 7. Implement a Transfer Learning concept in image classification. 8. Using a pre trained model on Keras for transfer learning. 9. Perform Sentimental Analysis using RNN. 10. Implement an LSTM based Auto encoding inTensorflow/Keras. 11. Image generation using GAN. ADDITIONAL EXPERIMENTS 12. Train a deep Learning model to classify a given image using pre trained model. 13. Recommendation system from sales data using Deep Learning. 14. Implement Object detection using CNN. 15. Implement any simple Reinforcement Algorithm for an NLP problem.

CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record

CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record

CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record

Asst.prof M.Gokilavani

The Educational Administration: Theory and Practice publishes prominent empirical and conceptual articles focused on timely and critical leadership and policy issues of educational organizations. The journal embraces traditional and emergent research paradigms, methods, and issues. The journal particularly promotes the publication of rigorous and relevant scholarly work that enhances linkages among and utility for educational policy, practice, and research arenas. The goal of the editorial team and the journal’s editorial board is to promote sound scholarship and a clear and continuing dialogue among scholars and practitioners from a broad spectrum of education. Educational Administration: Theory and Practice presents prominent empirical and conceptual articles focused on timely and critical leadership and policy issues facing educational organizations. As an editorial team, we embrace traditional and emergent theoretical frameworks, research methods, and topics. We particularly promote the publication of rigorous and relevant scholarly work with utility for educational policy, practice, and research. The journal’s primary focus is on studies of educational leadership, organizations, leadership development, and policy as they relate to elementary and secondary levels of education. Examinations of leadership and policy that fall outside K-12 are considered insofar as there are meaningful connections to the K-12 arena (e.g., college pipeline). International comparative investigations are welcome to the extent they have implications for a broad audience.s.

Call for Papers - Educational Administration: Theory and Practice, E-ISSN: 21...

Call for Papers - Educational Administration: Theory and Practice, E-ISSN: 21...

Call for Papers - Educational Administration: Theory and Practice, E-ISSN: 21...

GAS POWER CYCLES Cycles: Otto, Diesel, Dual, Brayton - Calculation of mean effective pressure - Air standard efficiency - Comparison of cycles INTERNAL COMBUSTION ENGINES Classification - Components and their function - valve timing diagram and port timing diagram - actual and theoretical p-v diagram of two stroke and four stroke engines – carburettor - diesel pump and injector system - battery and magneto ignition system - principles of combustion and detonation in CI engines - lubrication and cooling systems - performance parameters and calculations.

Thermal Engineering Unit - I & II . ppt

Thermal Engineering Unit - I & II . ppt

Thermal Engineering Unit - I & II . ppt

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Water Industry Process Automation & Control Monthly - April 2024

Water Industry Process Automation & Control Monthly - April 2024

Water Industry Process Automation & Control Monthly - April 2024

Online banking management system project.pdf

Online banking management system project.pdf

Online banking management system project.pdf

Call for Papers - International Journal of Intelligent Systems and Applicatio...

Call for Papers - International Journal of Intelligent Systems and Applicatio...

Call for Papers - International Journal of Intelligent Systems and Applicatio...

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Unleashing the Power of the SORA AI lastest leap

Intze Overhead Water Tank Design by Working Stress - IS Method.pdf

Intze Overhead Water Tank Design by Working Stress - IS Method.pdf

Intze Overhead Water Tank Design by Working Stress - IS Method.pdf

PVC VS. FIBERGLASS (FRP) GRAVITY SEWER - UNI BELL

PVC VS. FIBERGLASS (FRP) GRAVITY SEWER - UNI BELL

PVC VS. FIBERGLASS (FRP) GRAVITY SEWER - UNI BELL

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CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record

CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record

CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record

Call for Papers - Educational Administration: Theory and Practice, E-ISSN: 21...

Call for Papers - Educational Administration: Theory and Practice, E-ISSN: 21...

Call for Papers - Educational Administration: Theory and Practice, E-ISSN: 21...

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Thermal Engineering Unit - I & II . ppt

Thermal Engineering Unit - I & II . ppt

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SGI OpenFOAM Cloud Benchmark, OpenFOAM Study Meeting for beginner@Kansai, 7th Jul. 2012

1. Jul. 7th 2012 OpenFOAM Study Meeting for beginner @ Kansai Masashi Imano Masashi Imano SGI OpenFOAM Cloud Benchmark 1

2. Jul. 7th 2012 OpenFOAM Study Meeting for beginner @ Kansai Masashi Imano Test cluster conditions • Hardware: SGI Altix ICE8200 • CPU: Intel Xeon X5365 3.00GHz 4cores x 2 • Node: 16 nodes • Memory: 16GB/node (No swap memory) • Interconnect: InﬁniBand DDR • OS: SUSE LINUX ENTERPRISE SERVER 10.3 • MPI: ✓ SGI MPT (1.2.6) ✓ OpenMPI (ThirdParty package distributed with OpenFOAM) 2

3. Jul. 7th 2012 OpenFOAM Study Meeting for beginner @ Kansai Masashi Imano • OpenFOAM versions: 2.1.x • MPI: ✓ OpenMPI (Ver. 1.5.3, mpirun options: --mca btl openib,self,sm) ✓ SGI MPT (Ver.1.2.6) • Benchmark problem ✓ Flow: Lid-driven 2D cavity (tutorials) ✓ Re: 100 (laminar) ✓ solver: icoFoam (piso) ✓ Linear solver: AMG (p), PBiCG (U) ✓ Mesh: S(500 2,Δt:5x10-5 s), M(1000 2, Δt:10-5 s), L(2000 2, Δt:10-5 s) x Ux = 1 m/s d = 0.1 m y Calculation conditions 3

4. Jul. 7th 2012 OpenFOAM Study Meeting for beginner @ Kansai Masashi Imano Benchmark results 0 37500 75000 112500 150000 1 2 4 8 127000 140000 80000 15940 10810 21700 60000 37700 Timesteps/Day Number of Nodes OpenMPI SGI MPT Mesh: S (5002= 1/4 Millions) 4

5. Jul. 7th 2012 OpenFOAM Study Meeting for beginner @ Kansai Masashi Imano Benchmark results 0 2500 5000 7500 10000 1 2 4 8 8470 9240 5520 3260 4620 6240 4940 317 Timesteps/Day Number of Nodes OpenMPI SGI MPT Mesh: M (10002 =1 Million) 80%up 50%up 5

6. Jul. 7th 2012 OpenFOAM Study Meeting for beginner @ Kansai Masashi Imano Benchmark results 0 750 1500 2250 3000 1 2 4 8 2520 1560 510 280 1780 1430 450 260 Timesteps/Day Number of Nodes OpenMPI SGI MPT Mesh: L (20002 =4 Millions) 40%up 6

7. Jul. 7th 2012 OpenFOAM Study Meeting for beginner @ Kansai Masashi Imano Any Questions? 7