Visual Analytics, HPC, Simulations & AI

1. Visual Analytics,
HPC, Simulations & AI
Tomasz Bednarz (CSIRO Data61, UNSW Art & Design)
and
John Taylor (CSIRO Data61, DSTG)

2. CONFERENCE 17-20 November 2019 - EXHIBITION 18-20 November 2019 - BCEC, Brisbane, AUSTRALIA
SA2019.SIGGRAPH.ORG
About Tomasz
• Director and Head of Visualisation at the Expanded
Perception and Interaction Centre (EPICentre), UNSW Art &
Design.
• Team Leader (Visual Analytics) at the CSIRO Data61.
• Adjunct Associate Professor at the Queensland University of
Technology, Applied and Computational Mathematics.
• Individual Member at the Khronos Group.
• SIGGRAPH Asia 2019 Conference Chair.
tomasz.bednarz@siggraph.org | @tomaszbednarz

3. About John
• Group Leader (Computational
Platforms) at the CSIRO/Data61
• Program Leader, HPC and
Computational Science at the
Defence Science and Technology
• Adjunct Professor, School of
Computer Science, Australian
National University

5. CSIRO
Bracewell GPU Cluster

6. CSIRO Bracewell GPU Cluster
The most powerful supercomputer in Australia
CSIRO Bracewell GPU Cluster
6 |
• Bracewell consists of 114 PowerEdge C4130
servers hooked together with EDR InfiniBand.
• Aggregate memory across the entire system is 29
TB.
• Each server is equipped with four NVIDA P100
GPUs and two Intel Xeon 14-core CPUs.
• The GPUs alone represent over 2.4 petaflops of
peak performance.
• Bracewell was installed over a period of just five
days spanning the end of May and beginning of
June 2017.
• The system came online in early July 2017

7. CSIRO Bracewell GPU Cluster
7 |

8. Bragg Cluser Usage
• During 27 April – 27 May
• 50 users running GPU jobs
• 30,348 GPU jobs run
– Computational modelling
– Image processing
– Virtual nanoscience
– Molecular modelling
– Environmental modelling
– Physiological modelling
– Bioinformatics
– Machine learning
CSIRO Bragg GPU Cluster Usage
8 |
Source: CSIRO IMT Ahmed Arefin & Steve McMahon

9. SNAP – Simulated Nanostructure Assembly using Proto-particles
SNAP
9 |
Allows creation of user-defined nanoparticles, and subsequent Molecular Dynamics simulation to study
aggregation.
Nanoparticles are represented using a surface
mesh, enabling researchers to define complex
combinations of sizes, shape and
facet combinations, each with specifically defined
interactions.
GPU enabled to allow scaling to > 50,000 complex
zonohedrons.
Includes tools for generating nanoparticle surface meshes
and post simulation analysis.
https://research.csiro.au/mmm/snap/

10. Materials Informatics & Data-driven Discovery
Contact: Monolo Per, CSIRO Data61
10 |
• Analysis of High-Throughput Computation
• Data representation, Machine- and Deep-Learning approaches

11. CIFAR-10 Performance
System Processor Global Steps/sec
This laptop Intel Quad Core i7 0.237
Bracewell Intel Xeon 14-core E5-2690 0.95
Bracewell 4x Nvidia P100 GPUs 34.8
• Using the TensorFlow version of CIFAR-10 Convolutional Neural Network
• CIFAR-10 classification is a common benchmark problem in machine learning.
• The problem is to classify RGB 32x32 pixel images across 10 categories

13. Phaino – DL for Agriculture
Foivos I. Diakogiannis – Data61

14. Deep learning crop type classification
WA crops classification show accuracy of prediction of ~92%
Here we demonstrate the effectiveness of one of our models on unseen
validation data. The classifier “understands” the spatial coherence defined by
the borders of the various crops and uses this information to avoid salt-npepper
visual effects. The Generalized Dice coefficient on unseen validation
data (similar to F1 score for multiclass problems) is of the order of ~81%. The
accuracy ranges between 92-95%.
Foivos I. Diakogiannis, Peter Caccetta, Joanne Chia, Gonz Mata, Simon Collings, Drew Devereux, Suzanne Furby, Eric Lehmann, Tony Traylen,
Xiaoliang Wu, Zheng-shu Zhou

15. Protecting Consumers by Legal/Ethical Means
“We address this issue by proposing a formal framework
that can instantiate in agents’ dialogues moral/rational
criteria, such as the maximin principle and impartiality,
…e.g., by John Rawls’ theory or rule utilitarianism”.
digital-legislation.net
ML/AI technologies being ethical/legal Compliant-by-Design

16. • Developing HPC capability to support
defence research
• Pilot system has been acquired that
includes V100 GPUs
• Strong interest in application of AI
and deep learning to Defence
• Full system will be in the top 50 of
the TOP500 supercomputers
• Legacy codes including commercial
applications, eg CFD applications will
need significant work to run
efficiently on GPUs.
Presentation title | Presenter name
Defence Science and Technology
16 |

17. EPICENTRE
LABS
EPICYLINDER
DOME LAB
XR LAB
AVIE-SC
SUPER COMPUTERS

18. EPICYLIDNER

19. GENOMICS
VIEWER

20. DRUG
DISCOVERY

21. MASSIVE
NETWORKS

22. MASSIVE
NETWORKS

23. CREATIVE
MATH

24. CREATIVE
MATH

25. CREATIVE
MATH

26. POINT
CLOUDS

27. BLOOD

28. Automatic Site Selection of Cultural Venues
• A cGAN outputs zones from urban data as a constraint prior to a
stochastic optimisation of site locations of cultural venues.
Tian Feng & Tomasz Bednarz
28 |

29. Automatic Site Selection of Cultural Venues
• As cGANs can estimate appropriate zones for construction, the
search space is downsized and the optimiser quickly resolves
issues like centralisation and lack of public access.
29 | Tian Feng & Tomasz Bednarz

30. Transport Network Synthesis
• A cGAN is modified by replacing the binary classification function of the
discriminator with an energy-based function, and outputs functional transport
networks from elevations and densities.
30 | Tian Feng & Tomasz Bednarz

31. Transport Network Synthesis
• Instead of merely distinguishing real and fake examples, the energy-based
function aims to evaluate the traffic efficiency of a transport network using a
traffic simulation model.
31 |
Standard cGAN
Energy-based cGAN
Tian Feng & Tomasz Bednarz

32. Transport Network Synthesis
• Synthesised examples.
• Light red pixels stand for railways and dark red pixels for roads.
32 |
Synthesis Ground Truth Synthesis Ground Truth
Case 1
Case 2
Tian Feng & Tomasz Bednarz

33. Simulations - Specifications
Type Model Speed Sensor Weapon
AEW&C E-7A Wedgetail 955 km/h MESA
400 km
N/A
Jet EA-18G Growler 1,960 km/h AN/APG-79 AESA
150 km
AIM-9
35.4 km
AIM-120
105 km
AGM-88
75 km
GBAD NASAMS 100 km/h AN/MPQ-64 F1
75 km
AIM-120
75 km
Tank M1 Abrams 60 km/h AN/TPQ-48
10.2 km
M256 SBC
8 km
Humvee Bushmaster 100 km/h AN/TPQ-48
10.2 km
M240
3.725 km
* Weapon specifications were collected from ADF websites and Wikipedia.
Modelling Complex Warfighting Symposium

34. Simulations - Properties
• Weapon
• Type (AEW&C, GBAD, Humvee, Jet, Tank), ID (e.g., 0, 1, 2), Colour (Blue, Red)
• Coordinates (longitude, latitude, altitude), Heading, Speed
• Number of sub-weapons (e.g., AGM88 for jets, AIM120 for jets and GBADs)
• OnStation (only for AEW&Cs and jets), Target (only for jets)
• Track
• Tracker & trackee
• Corresponding sensor and weapon ranges
• Combat Network Adjacent Matrix
• Perron-Febonius Eigenvalue (PFE)1
1. J.R. Cares, An Information Age Combat Model
Modelling Complex Warfighting Symposium

35. Sensitivity Analysis
• Regression Estimation2 of PFE and Win Probability
• y1 = PFE of Blue force, y2 = PFE of Red force, y3 = Win Probability of Blue force
• x1 = no. of jets, x2 = no. of GBADs, x3 = no. of tanks and Humvees
2. J.P.C. Kleijnen, Sensitivity Analysis and Related Analyses
Modelling Complex Warfighting Symposium

36. Concept Demonstrator
Modelling Complex Warfighting Symposium

37. X, Y, Altitude
coordinates
Number of
alive turtles
Total number
of turtles
Sensor and
weapon range
PFE values of Blue
and Red force at
runtime Win probability of
Blue force at the end
of the battle at
runtime
Tracks from
Blue and Red jet
Simulation
runtime
Modelling Complex Warfighting Symposium

38. Visualisation PoC
Winner
Mean and STD of
PFEs of Blue Force
Mean and STD of
PFEs of Red Force
Modelling Complex Warfighting Symposium

39. Modelling Complex Warfighting Symposium

40. Modelling Complex Warfighting Symposium

41. Saving Jaguars – VR, Gaming, GPUs, Stats

42. Saving Jaguars

43. sa2019.siggraph.org
CG in Australasia
Tomasz Bednarz

44. CONFERENCE 17-20 November 2019 - EXHIBITION 18-20 November 2019 - BCEC, Brisbane, AUSTRALIA
SA2019.SIGGRAPH.ORG
Kangaroo
Image courtesy Tourism Australia
Lady Elliot Island, Queensland
Image courtesy Tourism Australia
Koala
Great Barrier Reef, Queensland
Unique Nature

45. CONFERENCE 17-20 November 2019 - EXHIBITION 18-20 November 2019 - BCEC, Brisbane, AUSTRALIA
SA2019.SIGGRAPH.ORG
CONNECTED CITY
Enjoy taking in the Brisbane river and city sights on a free
CityHopper ferry of City Loop bus.
Brisbane River
BRISBANE – AUSTRALIA’s NEW WORLD CITY

46. CONFERENCE 17-20 November 2019 - EXHIBITION 18-20 November 2019 - BCEC, Brisbane, AUSTRALIA
SA2019.SIGGRAPH.ORG
World’s best convention centre
BCEC was awarded the world’s best convention centre in 2016

47. CONFERENCE 17-20 November 2019 - EXHIBITION 18-20 November 2019 - BCEC, Brisbane, AUSTRALIA
SA2019.SIGGRAPH.ORG
Conference Program
• Technical Papers
• Courses
• Art Gallery
• Computer Animation Festival
• Emerging Technologies
• Virtual & Augmented Reality
• Real-time Live!
• Technical Briefs & Posters
• Birds of a Feather
• Featured Sessions
• Business and Innovation Forum
• SA2019 Demoscene
• Studio

48. www.data61.csiro.au
CSIRO Data61 & EPICentre
Tomasz Bednarz
Team Leader / Director of Vis
t +61 459 855 376
e tomasz.bednarz@csiro.au
w data61.csiro.au
w epicentre.matters.today
CSIRO Data61 & DST
John Taylor
Group Leader / Program Leader
t +61 400 997 446
e john.a.taylor@csiro.au
w data61.csiro.au
Thank you