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Every engineer running simulations has experienced the struggle of formulating a computation or simulation job and being unable to solve it due to hardware or software resource constraints.  At Rescale, our goal is to eliminate these constraints and ultimately empower engineers to move up a level of abstraction toward simulation- and model-based design [mbd].  On-demand access to hardware and software resources changes how engineers can approach and solve the next generation of engineering challenges.

Most engineers have a basic level of access to internal high performance computing (HPC) clusters and simulation software–but access to on-demand resources opens up a broad set of new engineering problems that can be solved.  Often, engineers are trained to solve their problems within these given constraints and do the best job they can with set timelines. This results in an under-representation for the real demand and opportunity if access to hardware and software were ubiquitous.  Access to on-demand hardware and simulation software resources dramatically changes how engineers are able to utilize simulation in the product development process.

Lowering barriers to entry
At the most basic level, access to on-demand hardware and software resources dramatically lowers the barrier to entry for engineers to start running computational analyses. Significant upfront investments in hardware infrastructure or annual software licensing fees are not required with on-demand access as they have been in the past.

Higher fidelity models
Simulation models with higher fidelity designs can often give more accurate and higher confidence simulation results.  With access to variable on-demand HPC resources, engineers can significantly increase the fidelity of simulation models without the need to invest in additional HPC hardware.

Broader design space exploration
Linearly scaling out simulations through parameter sweeps or designs of experiment (DOE) can be an extremely powerful way to better understand how specific elements of a model affect the outcome and provide critical engineering insights.  Access to on-demand resources has a dramatic impact on these applications–commoditizing the incremental cost of a simulation and providing engineers and scientists the ability to run as many scenarios concurrently as they desire.

Algorithmic design development
Engineering models for complex systems have millions of parts and parameters which can be varied by engineers–often making a complete brute force search [bfs] of the entire design space impractical.  Optimization techniques, statistical methods, machine learning algorithms, and other tools can be utilized to more intelligently explore these design spaces.  A single function evaluation for these tools can involve the execution of a full simulation model that requires significant hardware and software resources.  The ability to seamlessly execute these simulations is a critical enabler for simulation workflows that use this type of algorithmic approach.

Multidisciplinary analysis and co-simulation
Most complex products have several different engineering or scientific disciplines that influence the design.  Simulation tools are often utilized for multiple components of the design process, providing the opportunity to combine the simulation tools (e.g. using functional mockup interfaces [fmi]) to understand how design changes affect each discipline independently while exploring correlations and interactions between them.  When multiple simulation tools are connected together, the software and hardware requirements increase exponentially.  These types of multi-discipline approaches are often used in conjunction with algorithmic exploration due to the highly complex interactions between parameters (e.g. multidisciplinary design optimization [mdo]).  This often requires resource requirements beyond what has been traditionally accessible.  Co-simulation is another example of this, but a more tightly integrated concurrent approach.

Engineering management and budgeting
Executives and engineering management often undergo a very difficult resource allocation and budgeting process in an attempt to predict the hardware and software requirements for a one- to three-year period while investing in capital expenditures for HPC clusters and annual software licenses accordingly.  On-demand hardware and software resources allow for direct accounting and very granular budgeting approach.  With the ability to calculate returns on investment (ROI) directly for simulations and understand the computing costs on a project basis, management can empower engineering teams to dynamically allocate resources as engineering and business needs change.  This approach provides engineers the leverage to objectively assess the value of simulation tools and directly tie these investments to results.

Engineering and scientific simulation is becoming the critical tool that fuels the innovation pipeline for leading companies in industries and applications ranging from the development of new spacecraft and unmanned aerial vehicles (UAVs) in aerospace to revolutionary new drug discovery techniques in life sciences to advanced electronic design automation (EDA) for the next generation of semiconductor devices.  Access to on-demand resources for hardware and software provides engineers the tools to move up a level of abstraction in the development process. This dramatically improves the ability to handle large complex models and utilize simulation driven development as a key asset for innovation and breakthrough technologies.

[mbd]: http://en.wikipedia.org/wiki/Model-based_design
[bfs]: http://en.wikipedia.org/wiki/Brute-force_search
[fmi]: http://en.wikipedia.org/wiki/Functional_Mock-up_Interface
[mdo]: http://en.wikipedia.org/wiki/Multidisciplinary_design_optimization

This article was written by Joris Poort.

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If you’re like me, you’ve attended innumerable trade conferences in your career. This ancient history includes all the Web 1.0 events including Networld/Interop, Supercomputing, LinuxWorld, Cisco Partner Summit, IBM PartnerWorld and more recently, a number of software vendor sponsored automotive user conferences.

This past week I was fortunate to meet with many software developers and customers at the SAE 2014 World Congress in Detroit–attended by more than 9,500 automobile industry engineers and suppliers. The technical sessions at SAE are robust in number and deep in subject. Outside the sessions–on the exhibition floor itself–is where I made my notes and experienced a very strong sense that Silicon Valley and Motown are connected more closely than ever–in significant ways if you “raise the hood” and share chatter with industry colleagues.

Here are my takeaways from SAE last week:

1) Not business as usual. There is nothing unusual about noting the large section of technical computing independent software vendors (ISV) front and center on the show floor.Strolling the show floor or an online visit to sae.org/congress will expose the applications and global companies whose solvers are most commonly used in automotive design. When the topic of cloud-based simulation comes up with sales and engineering managers, there is almost universal understanding and excitement about what immediate convergence time and performance benefits can be derived from applications used today. In a business where performance metrics mean success or failure in the crash-test environment, time is of the essence. Engineers universally understand the frustrations of inadequate local resources in a deadline-driven IT environment and the elegance of bursting on demand to secure HPC-class resources and storage. In conversations with engineers from tooling, drivetrain, chassis, body design, audio engineering, safety and thermal analysis, it was quickly evident that there is a proliferation of engineering environments where basic computing resources are insufficient. Whether a function of local departmental IT budgets or competing utilization of internal cluster scheduling, on-demand cloud-based simulation is being viewed as a natural evolution in automotive design.

2) Get ready, Detroit. I have seen the future of American automotive engineering. A bold statement–but it was right there before my eyes.

All those fresh young faces from Ann Arbor, Lansing, and Houghton coming to SAE with their ME, EE and Computer Science degrees are not necessarily hopping the first flight to San Jose to go to work for the traditional Silicon Valley bellwether companies – or even the large number of venture-backed startups that populate San Francisco. They come from families steeped in the traditions of American auto manufacturing, and unlike years past, at SAE they find a job market in an explosive phase. In addition to special attendee rates, upon arrival they find mentoring and career planning sessions, and a remarkable number of booths staffed – not by salesmen hawking their wares – but HR managers and hiring managers ready to grab top talent.  There are career websites to be sure – but at SAE the physical racks with job prospectus packets stretched over 40 feet down an entire aisle. A gentleman with two tall, 20-something well-groomed young men stood at the end of the row. He was starting to gather material and agreed that this abundance of professional opportunity was a harbinger of a rebounding auto economy – something Detroit has desperately needed for so long.  What is most refreshing is the programmatic mentoring and professional counseling the automotive community is offering the new engineering generation. Their fresh ideas are being sought out in the labs and boardrooms of companies whose hiring was frozen during recent years of economic downturn. The staid boardrooms of Motown are in evolution.

3) Why is this important? Young engineers coming out of undergraduate or post-graduate engineering studies are not the first generation of computer or data center literate engineers. They are, however, the first of a generation exposed to the informational and organizational power of social networking, Google Apps, and immediately available information. This has brought about a literacy with the enormous potential of producing more accurate results obtained from computing power that matches the complexity of their algorithms. Today, ISV’s are engineering their solvers revisions[IG1]  to harness the enormous potential of cloud-based simulation. Increased design complexity and the industry’s competitive need to innovate, means automotive simulation modeling in the cloud offers ISV’s a net-new licensing revenue stream.

Conclusion: the intersection of new engineering talent, a robust job market, ubiquitous on-demand HPC power, and applications “born to run” are creating a perfect storm that can only be observed as a trend-all highly visible at SAE.

4) Lastly, automotive engineering is poised to explode in cloud simulation. Why? This key vertical market consumes a broad variety of compute-intensive applications from the front to rear bumper. Material composition, aerodynamics, electrical systems, engine and fuel efficiency, hybrid and electrical powertrain research, driver safety, interactive display and information systems, and comfort–all these complex calculations benefit from the availability of immediate compute, memory, and storage resources. Coincidentally, significant investment is being made by globally-adopted technical software developers–many of whom are either in full engagement with their cloud strategies or are launching in the near term. Global consultancies and engineering firms can now confidently engage with customers with cloud simulation options.

Automotive energy research and cloud-based simulation are a marriage made in heaven. Rapid advances in electric, hybrid and natural gas research will be a direct result of cloud-based compute power under the reins of the new generation of  engineers. On-demand simulation in the cloud offers Detroit and the global auto industry a dynamic resource, available today, that is already fundamentally changing the automotive product landscape. My conversations at SAE validated that cloud-based simulation is a key part of the calculation for a growing, energetic ecosystem of engineers, developers, and their channels.

This article was written by Rescale.

Rescale provides a suite of fully integrated simulation and workflow software tools, including, Abaqus, CONVERGE, LS-DYNA, Nastran, and Fluent, among others (for a full list please click here). Partnerships with the software vendors who have developed these software packages are an important element in ensuring customers readily have access to the tools when and where they need them.

One essential aspect of partnering with software companies is the availability of resources that help users quickly become comfortable with running the simulation software they’re familiar with on Rescale. Even with open source codes, we try to work with creators and/or contributors to develop easy-to-understand resources that guide new users on how to properly set up and execute simulations on Rescale’s customizable HPC platform.

These resources currently come in the form of written tutorials, step-by-step instructions, code examples (coming soon), and video tutorials. Tutorials work well for users who are interested in a thorough example and understanding of how to run specific simulation software on Rescale and the reason behind each step. However, if you’re limited on time but still interested in how to set up a job, our new section of video tutorials helps those you on a time crunch.

On Rescale’s Resources page, you’ll find videos that show you how to set up a basic job as well as simulation software specific video tutorials. Two recent video tutorial collaborations include our work with Siemens and Ricardo Software.

In the Siemens video, we go step-by-step through how to run an automotive application to predict sound pressure resulting from driving over a bump in the road using NX Nastran. You can see how to save an NX Nastran job in the native graphical user interface (GUI) and then upload the input files to Rescale, quickly enter the job parameters, and execute the simulation.

To execute a Ricardo Wave RDM simulation, the video tutorial below highlights the necessary steps to kick off a job. You can see how to save a 4-cylinder spark ignited engine model in WAVE, customize the hardware, upload the input file, and submit the job.

New resources, including videos, are constantly added to Rescale’s Resources page, so be sure to check back often. We’re always looking for new ideas and suggestions so if there is a tutorial you would like to see that is not listed, please contact info@rescale.com.

This article was written by Ilea Graedel.

On April 7th, we were made aware of a serious vulnerability in the popular OpenSSL cryptographic software library known as the Heartbleed bug. When it is exploited it leads to the leak of memory contents from the server to the client and from the client to the server.

We’ve taken a number of steps to address this issue:

  1. We’ve patched all of our systems using the newer, protected version of OpenSSL.

  2. We’ve generated and installed new SSL certificates and revoked our older certificates.

  3. We’ve reset all browser sessions that were active prior to the vulnerability being addressed on our servers. You will have to log back into Rescale the next time you visit our platform.

We will continue to monitor the situation and provides updates as appropriate. Please contact us at support@rescale.com if you have questions.

This article was written by Rescale Engineering.