In this deck from the 2019 Stanford HPC Conference, Ebru Taylak from the UberCloud presents: Innovative Use of HPC in the Cloud for AI, CFD & LifeScience. "Scientists today have access to advanced tools and software to simulate complex behavior and interactions within their disciplines. However, typical workstations often do not provide enough compute power to solve their problems. A scalable platform such as the cloud, provides more accurate results, while reducing solution times. In this presentation, we will demonstrate recent examples of innovative use cases of HPC in the Cloud, such as, “Personalized Non-invasive Clinical Treatment of Schizophrenia and Parkinson’s” and “Deep Learning for Steady State Fluid Flow Prediction”. We will explore the challenges for the specific problems, demonstrate how HPC in the Cloud helped overcome these challenges, look at the benefits, and share the learnings. Ebru manages marketing at UberCloud. Her background includes nearly a decade of experience in the computer aided engineering field, mainly focusing on automotive projects. She is also an entrepreneur and held previous roles in technical sales, marketing and customer services of technology companies. She holds a Master of Science degree from the George Washington University and a Bachelor of Science in Engineering from Istanbul Technical University. Watch the video: https://youtu.be/CqXNH9eHPeE Learn more: https://www.theubercloud.com/ and http://hpcadvisorycouncil.com/events/2019/stanford-workshop/ Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter

1. Innovative Use Cases of HPC in the Cloud,
featuring AI, CFD and Life Sciences
Ebru Taylak
HPC Advisory Council – Stanford Conference
Stanford University, February 2019

2. Ingredients for Innovation through CAE

3. UberCloud Experiments
With the mission to make HPC a reality for
every engineer:
 200+ cloud experiments conducted
 Scientists, Engineers, Software Vendors,
Cloud Providers joined
 Based on the findings, UberCloud
Containers for CAE have been developed
Sponsors
UberCloud Experiments
Innovation & Use Case Awards

4. UberCloud Containers
Accessibility
Portability
Usability
Packagability
UberCloud Containers – Based on Docker with
additional HPC features and tools.

5. UberCloud Container Experience

6. Featured Use Cases
 Cloud Simulations enabling Deep Learning for Fluid Flow Prediction
 Cloud Simulation of Neuromodulation in Schizophrenia

7. Background:
 Fluid flow problems using Computational Fluid Dynamics (CFD)
require large computer processing power and simulation duration
 As an alternative to CFD simulations, a study has been conducted
to predict fluid flow around a given object using Artificial
Intelligence
Cloud Simulations enabling Deep Learning for Fluid Flow Prediction

8. Objective:
 Decrease time-to-solution while preserving the accuracy of a
traditional CFD solver
Challenge:
 Large amount of data samples were needed for ANN learn the
dependencies between simulated design and flow field around it in
order to accurately predict flow behavior
simulation set up
Cloud Simulations enabling Deep Learning for Fluid Flow Prediction

9. Deep Learning Workflow
Cloud Simulations enabling Deep Learning for Fluid Flow Prediction

10. HPC Simulation and Results
*
**
Cloud Simulations enabling Deep Learning for Fluid Flow Prediction
Training Data Generation and ANN Training
Time for 10000 simulations - local 13 h 10m
Time for 10000 simulations - cloud 2h 4m
Time for training 23.7h
Neural Network Prediction of Flow Field vs CFD simulation
Avg. Time for CFD solver - local 4.7s
Avg. Time for CFD solver - cloud 0.74s
Time of neural network prediction 3ms

11. Cloud Simulations enabling Deep Learning for Fluid Flow Prediction
Simulated Flow Field vs Predicted Flow Field
Exemplary simulated flow field (left image) and predicted flow field (right image)
 Training of neural network was much faster using large dataset of samples
 Higher accuracy was obtained through large dataset of samples
 Overhead of creating high volume of samples can be easily compensated through HPC
Cloud

12. Cloud Simulation of Neuromodulation in Schizophrenia
Background:
 Schizophrenia currently affects 1% of World’s population
 Treatment includes drugs, therapy, and deep brain
stimulation (DBS) through surgery
 Transcranial Direct Current Stimulation (tDCS) is a new form of
non-invasive neurostimulation involves the injection of a weak
electrical current to the head through surface electrodes to
generate an electric field that selectively modulates the activity
of neurons
 tDCS requires to be personalized depending on individual’s
brain morphology and skull architectureIllustration of transcranial Direct
Current stimulation device
Illustration of DBS through
surgery

13. Cloud Simulation of Neuromodulation in Schizophrenia
Objective:
 Develop a method for the clinician to access in real time and
reduce overall computational effort - where doctors can choose
two pre-computed electrical fields of an electrode pair to
stimulate specific regions of the brain
Challenge:
 Since each patient’s brain can be vastly different, an optimal
electrode placement needs to be identified on the scalp in
order to create the desired stimulation at specific regions of the
brain for an effective outcome

14. Cloud Simulation of Neuromodulation in Schizophrenia
Workflow for Virtual Deep Brain Stimulation
MRI Scan
FEM:
Computational
Model
Electrode
Placement
Choose Two
Electrode Pairs
Temporal
Interference
electrode placement chart

15. Localization of the peak Electrical Potential Gradient value in Abaqus for different
combinations of electrodes.
 26 simulations in the cloud– each representing a
different electrode configuration
 Simulation models contain 1.8M finite elements
 Single run on a local cluster with 16 cores, took about
75 minutes, compared to 28 minutes on 24 cores
HPC Cloud Cluster
 Running all 26 simulations in parallel brings overall
simulation time down from 33 hours to 28 minutes –
speed up factor of 70!
 Results are promising, however, there is still a lot of
work to be done in collaboration with the
Doctors/Clinicians at NIMHANS and other
Neurological Research Centers on how this method
can be appraised and fine-tuned for real time clinical
use
Cloud Simulation of Neuromodulation in Schizophrenia
HPC Simulation and Results

16. Take Aways
UberCloud CAE Containers provide engineers and scientists:
• A seamless way to run and manage the most complex engineering
workflows in the cloud
• Desktop like user experience with no new tools to learn
• The necessary boost in their simulation performance enabling innovation