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CFD – an On-Demand Service?

More and more CFD users are finding they have a requirement for large processing power. As simulations get more complex and with a higher resolution, users really need the extra compute power to get a sensible completion time on their tasks. Mesh sizes seem to be growing rapidly; I’ve seen engineers generate meshes of three billion cells – definitely not the job for your workstation!

Image of bike using CFD

More CFD users are finding they have a requirement for large processing power

How does a company meet the computing requirements for a huge variety and size of workloads? Well, fortunately, CFD applications scale well using HPC, but is the answer an in-house HPC cluster? I would consider this to be very costly and requires serious cost benefit analysis. I would recommend sizing your own system, because the cost of unused resources at quiet times becomes prohibitive. Ideally, a firm should purchase an in-house HPC system, which can handle maybe 80 per cent of its workload, with the largest 20 per cent of jobs burst to an external HPC-on-demand service. This gives flexibility and the ability to meet peaks in workload with pay-per-use services.

On-demand World
In my experience, users of CFD software are using a general CFD application or they have possibly compiled their own application, a variant of CFD with additional modules. They are – almost without exception – all very technically competent and more than happy using command line to manage jobs. However, they still want to retain the ability to work within a Graphical User Interface from their desktops.

On the same point, effective remote visualisation is also a vital component of an on-demand service and should be available for users, enabling the manipulation of large data sets and creation of complex visualisations remotely. At OCF, we are working with DragonHPC and others to refine this aspect of our own service.

Considerations
Licensing in an on-demand model is a critical – and potentially a very costly factor. Open source codes such as OpenFoam and Code Saturne are free and therefore ideal for on-demand computing. On our own service, for example, both of these applications are loaded and ready to use. By contrast, licensing to run Fluent on multiple cores is costly even for large businesses.

In my view, traditional license models are in fact the major limiting factor for CFD users’ adoption of on-demand access to HPC. We are, however, seeing the early stages of moving toward more flexible license models.

I recommend that users should also carefully evaluate the service and system performance. Check the total cost of using the service, not just the headline pence per core hour, as hidden costs, pricing thresholds and system performance all need to be taken into account. Users of the service also need visibility of spend levels at all times.

XFlow-Fighter-Jet

The future is looking bright for CFD users

As “cloud” adoption accelerates, the key barrier to CFD usage of HPC clouds is certainly the traditional license model. With new packages from companies like ENGYS and FlowHD taking this issue by the scruff of the neck, the future looks far brighter for CFD users. If you have any views or have experienced CFD software first-hand, I would be interested to hear from you.

You may be interested in reading the full article on this published earlier this week on CFD Review.

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