CO2 reduction, the increasing complexity of new powertrain systems, and a requirement to achieve the highest possible level of process efficiency are some of the key challenges facing the automotive industry now and for the foreseeable future. Research and further innovations are essential, but need to be tested in models and studies. These studies involve large-scale variations in parameter and require high levels of CPU cycles on-demand. Not only SMEs, but even larger companies, struggle to provide sufficient computational resources necessary for the simulations.
AVL is a company active in the development of powertrain systems for internal combustion engines. Many projects in the vehicle optimization area involve studies with large-scale variations in parameter and components which require high levels of CPU cycles on-demand. The difficulty is that a majority of companies lack of sufficient computational resources necessary to accomplish optimization tasks in an acceptable time-frame.
Using the European Commission Fortissimo project High Performance Computing (HPC) ressources, AVL developed powerful state-of-the-art physical models which are based on cloud High-Performance Computing. Their models has been able to demonstrate the viability of on-demand computing resources in the design of powertrains with specific emphasis on the reduction of CO2 emissions. This solution involves the running of AVLs simulation codes on a Cloud-based HPC system where computer resources are made available on-demand.
The most clear cost benefit of using HPC-cloud resources is the possibility to lease a powerful computing cluster for single projects instead of acquiring and maintaining computational resources which would be underutilized for most of the time, and probably even not sufficient when really needed. Using a Cloud-based solution, taking into account all additional cloud overheads, short-term projects running millions of simulations on 400 cloud CPU cores for a period of a couple of weeks, several times a year, would run with costs reduced by up to 90% when compared to the total cost of ownership of a dedicated in-house system. This is the cost range where it becomes attractive for SMEs to participate in projects which require high CPU power for only a short time.