porting and scaling engineering applications in the cloud · pdf fileubercloud community &...

Porting and scaling engineering applications in the cloud

Wolfgang Gentzsch, UberCloud

UberCloud Community & Industry Validation

3000+Community Members

200+HPC Cloud Experiments

Lessons from the experiments go directly

into the product

Established expertise and thought leadership

40+Marketplace Stores

Online access to

CAE technology

and expertise

50+HPC Containers

ANSYS, CD-adapco,

COMSOL,GROMACS,

NUMECA, SIMULIA, etc

Porting scenarios

Your application

Your container

Your servers

Any HPC Cloud

Your Workstation(s)

Benefits of Public Clouds

• More (infinite) computing

• No upfront Cap-Ex investment

• On demand, pay per use, at your fingertips

• Scaling resources dynamically, up and down

• Always the latest hardware and software

• No long procurement, nor acquisition cost, nor high TCO

• No need for expensive on-premise infrastructure & experts

• Choice, with multiple providers

10 Guidelines for Porting Your Application to the Cloud

1. Evaluate benefits of cloud vs on-premise for your specific scenario:On-premise or bursting or hosting or hybrid cloud? More jobs (more parameters) or larger models or ‘more‘ physics (FSI)

2. Look for existing cloud benchmarks: kernels, solvers, similar applications, and consider vendor and in-house benchmarks: on in-house workstation and server vs cloud node and server

3. Cloud providers might help with your benchmarking4. Cloud case studies and articles: contact / consult their authors 5. Different porting scenarios for in-house, open-source, commercial

software

10 Guidelines for Porting Your Application to the Cloud

6. Find out level of scalability of your application software => what scales on your in-house system should scale in the cloud

7. Select HPC cloud provider(s) with: powerful compute nodes, large memory, Infiniband, GPUs,…, user-friendly access, batch and interactive,… Azure, Bull/Atos, CPU 24/7, Nephoscale, Nimbix, R-Systems, Sabelcore, etc.

8. Consider HPC software container for your application: ease of packaging, porting, accessing, using, and scaling, plus maintenance and support

9. Perform a trial / proof of concept first10. Start with your existing model, compare results, increase in small steps

Porting in-house, open source, and commercial software

In-house Software- Pro: flexibility, fully under your control, no licensing, and in-house s/w expertise - Con: proprietary, high dev/maintenance/porting cost, and in-house s/w expertise- Cloud: building your own cloud environment and port yourself

Open Source Software- Pro: community effort, no s/w dev and maintenance, no licensing, - Con: often bugs, no reliable service and support, in-house s/w expertise- Cloud: building your own cloud environment and port yourself or the community

or cloud providers have done it already and offer it as a service

Commercial Software- Pro: stable, reliable, 24/7 support & maintenance, regular updates- Con: not flexible, expensive, limited capabilities (e.g. #cores), vendor lock-in- Cloud: ‚self-porting‘ not possible, either ISV or UberCloud have done it, licensing

often limited, vendor dependent, BYOL only with existing license, no easy upgrade/scale

Looking for the right HPC Cloud ProviderBenchmarking of cloud vendors with High Performance Linpack*

• Azure, AWS, IBM SL, Rackspace, NERSC

• Azure HPC scalability is comparable to an HPC Cluster at scale

• AWS still the first Public Cloud provider people think of, but in HPC it is not

• Other public clouds struggle in HPC (e.g. IBM Softlayer)

Mohammad Mohammadi, Timur Bazhirov, Exabyte Inc. Feb 2017

*Solver for large algebraic equation systems

Team 171:

UberCloud Team 171: Dynamic Study of Frontal Car

Crash with UberCloud ANSYS Container in the Cloud

Simulation times for different numbers of cores for a mesh model with 17K elements. Conclusion: number of elements is too small for higher number of parallel cores!

End-User: Praveen Bhat, Technology Consultant, INDIA. Software Provider: ANSYS in UberCloud Container. Resource Provider: Nephoscale in California

UberCloud Team 180: CFD SpeedIT on GPUs in the Cloud *

Test cases from top left: AeroCar & SolarCar - geometries from 4-ID Network; motorBike - geometry from OpenFOAM tutorial; DrivAer - geometry from Institute of Aerodynamics and Fluid Mechanics at TUM

Scaling OpenFOAM on 2 clusters from Ohio Supercomputing Center: Oakley with Intel Xeon X5650 processors and Ruby with Intel Xeon E5-2670 v2 processors, and on a single GPU (NVIDIA Tesla K40), using SpeedIT Flow

*) Case Study Author – Andrzej Kosior, Vratis Ltd.

Team 181

UberCloud Team 181: Prediction of Barehull KRISO

Containership Resistance with NUMECA in the Cloud

**

*) UberCloud Container on CPU 24/7

Comparing NUMECA FINE/Marine simulation resultson different clouds with corresponding experimental result

UberCloud multi-container multi-node environment

UberCloud Team 175: Parametric Radio Frequency

Heating with COMSOL Multiphysics in the Cloud

This Radio Frequency (RF) heating application simulates dielectric heating of an insulated block, caused by microwaves travelling in an H-bend waveguide.

RF model: a large number of parameters need to be computed. It can be parallelized so that several frequencies and geometric parameters are computed at the same time. This model yields what is called an embarrassingly parallel computation.

Join me at a Live Demo

Aerodynamics of a Motorbike

https://naca4412.theubercloud.net

Thank you!Wolfgang Gentzsch

UberCloud President

wolfgang.gentzsch@theubercloud.com