System Modeling and Digital Analysis Ecosystem for SMEs on Amazon EC2

Brian Schott, CTO

Nimbis Services, Inc.

About Nimbis Services

Nimbis lowers the barrier to high-performance computing (HPC) through pay-as-you-go access to pre-configured technical computing software on cloud and HPC systems.

We offer remote-desktop and tailored web-portal access to digital simulation and analysis software primarily for the design and manufacturing sector.

We offer gridMathematica on Amazon EC2 for 1.5x the instance-hour price.

The “Missing Middle” in High Performance Computing

Nimbis was founded in 2008 motivated by USC and Council on Competitiveness studies on the lack of adoption of high performance computing in the U.S. manufacturing supply chain.

Access to talent, lack of easy-to-use software, and not understanding the ROI of high performance computing were significant barriers for virtual prototyping.

These studies have led to national and regional initiatives in digital manufacturing.

(SystemModeler^TM) for the Artisan Process

Lessons learned in early pilot studies with small manufacturing enterprises (SMEs):

SMEs rely heavily on tribal knowledge and follow an artisan process.

New tools and materials can be disruptive.

Computing capability often limited to CAD.

Available modeling software is high-fidelity, but single physics, with difficult-to-master domain-specific tools.

Firms may have separate structural finite-element analysis (FEA) and computational fluid dynamics (CFD) engineers.

Real-world problems involve multiple physics domains and many design parameters, which is beyond desktop.

We think SystemModeler is promising for SMEs since it enables an artisan process in the digital domain. It is a very general environment that handles modeling of systems across multiple domains with instant visualization and analysis.

What and Why: Cloud Computing?

Ignore the hype surrounding cloud. The important features are on-demand self-service and metered service pricing. This makes it technically possible and potentially cost effective to adjust to varying workloads through horizontal scaling or vertical scaling.

Vertical Scaling

For example, on-demand pricing on Amazon EC2 ranges from $0.02/hour to $4.93/hour.

Horizontal Scaling

The biggest bang for the cloud buck can be found with horizontal scaling.  Since we are drawing from a large pool, the rational strategy is to scale to as many servers as is feasible.

Embarrassingly parallel parametric sweeps* and Monte Carlo simulations.

Design optimization involves adaptive parameter adjustment to find the best solution.

Distributed simulation and analysis means connecting individual subsystem simulations together to model a more complex system that doesn’t fit on a single desktop.

Keep in mind that Amazon charges by the instance-hour and failures happen.

* our demo

SystemModeler and gridMathematica on Amazon EC2

Demonstration Application

CauerLowPassSC low-pass-filter of the fifth order.

http://reference.wolfram.com/system-modeler/libraries/Modelica/Modelica_Electrical_Analog_Examples_CauerLowPassSC.html

Demonstration Software

Ubuntu 13.04 (Raring Ringtail)

gridMathematica 9.0.1 Linux

SystemModeler 4.0-pre3 Linux

Demonstration Setup

Amazon Elastic Compute Cloud (EC2) US-EAST-1 Region

1 x High-CPU Extra Large Head  (7GB mem, 1.6TB disk, 8 cores, $0.58/hour)

10 x High-CPU Extra Large Workers (7GB, 1.6TB disk, 8 cores, $0.58/hour)

Conclusion

SystemModeler and gridMathematica on cloud makes a powerful system modeling and digital analysis ecosystem for the small manufacturing companies we see across the country.  Nimbis hopes to role this out as a cloud-hosted product in the future.  Stay tuned.

Summary of Simulation Results:

One Local Simulation: 24s  (compilation overhead included)

Eight Remote Simulations: 28s (compilation overhead included)

121 Simulations on 1 Instance: 320s

121 Simulations on 8 Instances: 251s (Inefficient mapping)

121 Simulations on 8 Instances: 60s (Efficient mapping)

This was commodity public cloud hardware, expect even better efficiency on cluster compute instances with 10GbE and HPC-class processors.

Acknowledgements

I’d like to thank Joe Bolte, Jeff Todd, and Angela Sims for support on this project.

Questions?