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I’m writing this article from my living room, because... well, you know the reason. COVID-19 has changed a lot. One impacted aspect is how and where we work. When employees were forced into home office, organizations struggled to build up the necessary infrastructure for this new reality. The situation exposed how unprepared our economy is for the coming challenges. Even before the pandemic, traditional manufacturing companies were under enormous pressure to update their product portfolio and find new business models, threatened by socioeconomical earthquakes like digitalization and the climate crisis. They must now rapidly transform into “agile” and “digital” operations. During the pandemic, it became apparent that these colorful words are too often still merely marketing slogans and do not have a lot to do with the everyday life of an engineer.
One technology is central to the necessary transformations of the industry: the cloud. The cloud enables new services, business models, and previously unknown development speeds by making data and resources universally accessible. It is especially powerful in engineering, where the success of new product designs often hinges on the accessibility of the best hardware and software.
This article covers the relationship of the cloud and “CAE”. Computer Aided Engineering (CAE) is a discipline that bundles digital design and optimization tools for mechanical hardware products. The cloud is a vaguer concept. At the end, it is nothing more than a network of servers you do not buy but rent. Most industries use it every day as a key enabling technology for new business models, digital products, and services. It is the heart of the most commercially successful companies on the planet. One successful example is Office 365 by Microsoft, that makes working on documents easier and more collaborative.
A well-known concept which describes the market entry of new technologies and services is the Innovation Adoption Lifecycle (see Figure 1). It segments the market for any given product into 5 categories. Here, us engineers are among the “laggards” or "late majority". We are often skeptical of new technologies and prefer sticking to proven routines.
When we discuss running CAE in the cloud with our clients and partners, similar questions often arise:
In the following sections, I will go over each of these points, discuss the data and outline some potential pitfalls. By the end, you will have general understanding of the status quo of CAE in the cloud and the big picture when looking ahead.
Disclaimer: I am of course a cloud native engineer. Everything I say should be taken with a grain of salt. Still, I will try to be as unbiased as possible and focus on the hard facts, leaving the interpretation up to you.
CAE is all about computations and simulations (e.g. structural mechanics or Computational Fluid Dynamics). Organizations computing large quantities at great speeds will optimize their products more efficiently and thus gain a significant competitive advantage. This is where the power of the cloud comes into full force.
In the past, businesses needed to invest in an on-premises HPC system. This is not only expensive, but it also has a crucial drawback: you operate on a fixed capacity. In engineering reality, workloads fluctuate with changing business demands. Today, you might need to run three simulations and tomorrow 500 to get the project done. An on-premises HPC system is a key resource shared by many users. At full capacity, it becomes a bottleneck hindering crucial design studies and delaying innovation. In the cloud you receive an “infinite” capacity and you always have just the computing power you need at any given moment. Figure 2 shows how the fixed capacity of a data center limits your simulation throughput.
With the cloud, engineers can also easily test out new hardware and software to find the optimal solution for any given engineering task. This improves engineers’ productivity, allowing them to use tools for a specific time or project only or to test out different approaches for a given task. As the CAE world becomes more diverse with even more specialized algorithms, this brings crucial speed to your R&D. Cloud subscription models will include everything you need to start working right away. This simplifies scaling software capacities and adding features or hardware power, aligned with your evolving needs.
Who likes dealing with Linux installations on servers? This is the reality you face when implementing an on-premises HPC solution. After installation, you and your IT department carry the ongoing burden of keeping up with maintenance and upgrades. Cloud providers offer fully functioning turnkey solutions including hardware, software, and service and take responsibility for downtimes, maintenance, and updates.
This saves valuable time that is normally spent in the procurement process, installation, and maintenance that can now be used to focus on new product designs. The cloud reduces the need to own IT infrastructure, making your organization slimmer and more agile, outsourcing the risk of hardware investments to your cloud provider.
Cloud providers make sure that you always receive the newest and best hardware on the market. This is a key advantage because hardware is getting more diverse. Consider the latest advantages of general-purpose GPUs (Graphics Processing Units) and FPGA (Field-Programmable Gate Array) chips. Modern CAE algorithms depend heavily on running on the optimal hardware, making it increasingly difficult to accommodate all needs of CAE when investing in on-premises capacities.
Combined with cloud technology, CAE data becomes accessible for many stakeholders across different departments in the organization, such as design, research, development, and product lines. This enables collaboration and communication between the parties. Simply share your projects, results and reports with a few clicks or discuss the effects of new design variations in real time.
In a cloud-based environment, remote and distributed work (remember the pandemic?) work out of the box. Cross-functional and international engineering teams can access all design tools and projects from anywhere and at any time, no matter what device is used. A simple laptop or tablet plus a browser is all you need.
In the end it boils down to only one thing - making your engineering more productive, so that you can focus on what truly matters: optimizing product designs faster and fostering innovation. This improves your organization’s competitiveness in rapidly changing markets.
Often you hear that the cloud is 2 to 5 times more expensive than investing in a server. This is not true. Let us clear up some misconceptions.
Firstly, when comparing a cloud-based solution with an on-premises solution you risk an apples to oranges comparison. As outlined above, the two models behave completely differently and might even serve other purposes. Keep this in mind when analyzing costs. Still, I will try to build up a fair comparison.
Secondly, when you determine the cost of a cloud solution, you must consider the concrete pricing model of your cloud provider. A variety of options exist, suited for different business demands. Some offer more flexibility (“on-demand”) for a higher base price. Others provide more fixed models, closer to the on-premises world (e.g. renting a cloud server for 3 years). You need to decide which model fits your needs best, both financially and technically.
Thirdly, cloud computing is an Operational Expenditure (OPEX), whereas on-premises hardware requires Capital Expenditure (CAPEX). CAPEX typically involves complicated procurement processes and needs to be justified in front of upper management. Also, investing is a financial risk. What happens if the expected ROI of the server does not materialize? What happens if priorities change and instead of CPUs, you need GPUs for the next design project? Often the amortization of an IT investment takes a very long time or never happens.
Lastly, make sure to consider all costs involved in CAE. Often, just the bare metal hardware and software licenses are compared. This is a misleading metric because those are just the tip of the iceberg.
Now, let us crunch the numbers. When purchasing a standard HPC system with 256 CPU cores, you will pay roughly € 125k for hardware. Your TCO over 3 years will be:
How does this compare to the cost in the cloud? Say you rent CPUs of the Type Intel Xeon Platinum 8168 with 44 Cores on Azure. This will cost you roughly € 2.67 per hour, giving you € 0.06 per core hour. If you purchase your core hours through a third party, you will typically pay € 0.10-0.20.
It now comes down to one key metric: the utilization of your servers. Even when you are not there, your server still is. The effective core hour price is:
Core Hour Price = TCO / (Available Core Hours x Utilization).
In the example, our on-premises server has
256 Cores x 24 h x 365 d x 3 yrs = 6.727.680 Core hours
available. At 70 % average utilization, the effective core hour price is
€ 625.000 / (6.727.680 Core hours x 0,7) = 0.13 € / Core hour.
Table 2 lists the effective core hour prices from 10 to 100% utilization.
In this simple example, with a price of € 0.10 in the cloud, you need to achieve an average utilization of over 90 % to justify the investment. This is almost impossible to achieve in real life.
A simple example has shown that the cloud is competitive with an on-premises system when you consider all costs. What we left out are changing demands in the future. HPC is a field of growth and you might need to update your infrastructure to support new usage scenarios. New hardware types (GPUs, FPGAs, etc.), multiple interconnect technologies (InfiniBand, Omnipath, etc.) and codes highly optimized for certain hardware configurations make strategic buying decisions increasingly difficult. To complete the business case, you must also include the opportunity cost of an on-premises server, for example by throttling your simulation output and failing to materialize the significant ROI of HPC.
There is one potential showstopper that is brought up in every discussion about CAE in the cloud at some point: data security. Security is a crucial aspect in CAE because engineers handle some of their organizations’ most sensitive data.
To secure clouds, various technical measures, processes, and contractual obligations between provider and client are employed. These include communication encryption (HTTPS, VPN) between clients and in the cloud network, and encryption of keys and discs. An arsenal of stricter measures exists, such as digital signature checking and single-tenant environments.
Now that we eliminated the roadblocks, it is time to get your hands dirty. When you start the journey to the cloud, you enter a diverse world of new technologies and possibilities with which you should familiarize yourself before defining your own cloud roadmap.
The world of cloud CAE is growing and maturing. Tools across the whole CAE chain are available, ranging from CAD, FEA (Finite Element Analysis) simulation, electronics simulation, CFD to PLM (Product Lifecycle Management). Cloud CAE has long been a domain of bold startups, sensing the reluctance of traditional vendors to invest in the new technology. However, this seems to be changing. Most of the household names in CAE now try to strengthen their profile in the field, updating their license plans to comply with the changing requirements of the cloud, and some have even launched home-made cloud solutions.
It is important to consider the different models available. First make a fundamental decision on your approach to the cloud:
Only the public cloud will materialize the values described earlier, as it gives direct and flexible access to a powerful data center, thus enabling higher CAE productivity. For some organizations, a hybrid solution could be of interest, covering baseloads with an on-premises system and shifting peak loads to the cloud.
On the public cloud, you will then need to consider the adequate level of service for your case:
SaaS applications are developed by a third-party vendor, whose interface is accessed on the clients’ side. SaaS includes everything you need to start working right away. You usually have less possibilities to customize solutions, but you will also not need specialized IT know-how to get going.
In CAE, vendors take different approaches to enabling the cloud, with different degrees of cloud adoption. One approach is “Cloud plugins” that offer easy interfaces to migrate existing CAE workflows to the cloud. This is targeted at users who are looking at the cloud for outsourcing peak demands while keeping on-premises capacities. Traditional software vendors typically position themselves on this level. “Cloud natives”, on the other end, provide a complete turnkey cloud solution for specific CAE workloads, including pre- and post-processing and dedicated support teams.
Moving forward, you need to choose the right implementation model for your needs. Consider your CAE intensity (number of simulations run per day), sensitivity of data, your existing in-house knowledge about cloud, and your company strategy when choosing an appropriate technology stack.
The migration of CAE workloads to the cloud will most likely be part of a broader company cloud initiative. Outline a roadmap for the transition to cloud CAE to avoid misalignment with the holistic company cloud strategy. You should identify all stakeholders of the CAE process (IT, design, sales), learn who else benefits from the initiative, and get input on the technology stack decision. Also, consider first migrating one entire CAE workflow to the cloud to reduce transition risks.
The cloud will create new possibilities for engineering. Fully automated design studies, access across different cloud platforms, and straightforward connection of tools through API (Application Programming Interface) access drastically broaden the spectrum of how simulation is employed and make the remarkable work of simulation engineers accessible to everyone. Already, companies have started integrating simulations with IoT (Internet of Things) and digital twin applications to determine the health of complex systems in operation with the help of sensor-enabled simulations. Simulations will become simpler, enabling design engineers to participate in the process (“Democratization”). At the same time, they will become more sophisticated to satisfy the demand for ever more complex multi-physics simulations and ever bigger models.
Can you imagine all this in an on-premises world? Well, I can’t. Imagine a world, where hardware does not play a role anymore. The project is due tomorrow? No worries, just spin up 10,000 cores. A big DoE (Design of Experiments) is required to find the best design? We’ve got you covered, run 100 simulations in parallel. You have no clue what tool to use to get to the bottom of your design question? Easy one, just test the SaaS applications out there in a quick trial. This is not some bright and distant future. It is today. We have finally arrived in a world where engineers can focus on what they are truly good at – improving product design, being creative and dancing with the physics. No wrestling with hardware, no IT-hustle, no overhead. Just engineering.
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