Porting and scaling engineering applications in the cloud. Wolfgang Gentzsch, UberCloud

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1 Porting and scaling engineering applications in the cloud Wolfgang Gentzsch, UberCloud

2 UberCloud Community & Industry Validation Community Members HPC Cloud Experiments Marketplace Stores HPC Containers Established expertise and thought leadership Lessons from the experiments go directly into the product Online access to CAE technology and expertise ANSYS, CD-adapco, COMSOL,GROMACS, NUMECA, SIMULIA, etc

3 Your Workstation(s) Porting scenarios Your application Your servers Your container Any HPC Cloud

4 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

5 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 inhouse workstation and server vs cloud node and server 3. Cloud providers might help with your benchmarking 4. Cloud case studies and articles: contact / consult their authors 5. Different porting scenarios for in-house, open-source, commercial software

6 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 first 10. Start with your existing model, compare results, increase in small steps

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8 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

9 Looking for the right HPC Cloud Provider Benchmarking 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

10 UberCloud Team 171: Dynamic Study of Frontal Car Crash with UberCloud ANSYS Container in the Cloud End-User: Praveen Bhat, Technology Consultant, INDIA. Software Provider: ANSYS in UberCloud Container. Resource Provider: Nephoscale in California 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!

11 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 E v2 processors, and on a single GPU (NVIDIA Tesla K40), using SpeedIT Flow *) Case Study Author Andrzej Kosior, Vratis Ltd.

12 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 results on different clouds with corresponding experimental result

13 UberCloud multi-container multi-node environment

14 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.

15 Join me at a Live Demo Aerodynamics of a Motorbike

16 Thank you! Wolfgang Gentzsch UberCloud President