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Simplify HPC Licensing for Parametric Studies

Ansys offers a single solution for enabling simultaneous execution of multiple design variations using standard high-performance computing licenses.

Ansys offers a single solution for enabling simultaneous execution of multiple design variations using standard high-performance computing licenses.

Ansys 2020 R2 offers a single solution for enabling simultaneous execution of multiple design variations using standard HPC licenses. Image courtesy of Ansys.


The pressure for designers to innovate—and to do so quickly—has never been greater. Having the ability to explore and test multiple design variations at once is critical, but this approach can be time-consuming without sufficient compute resources.

Parallel computing provides the means to solve multiple design variations simultaneously and efficiently. However, traditional software licensing models can complicate this approach, since multiple costly licenses would normally be required. Ansys has solved this problem in Ansys 2020 R2 with a single solution for enabling simultaneous execution of multiple design variations using standard high-performance computing licenses.

You can learn more about Ansys flexible approach to HPC licensing by viewing the “Ansys 2020 R2: HPC Licensing Update” on-demand webinar.

Final design performance and integrity can be maximized when designers are able to explore the widest range of material properties, manufacturing processes and real-world operating conditions. HPC-based parametric licensing for Ansys 2020 R2 provides this capability, and does so without cost-prohibitive, individual solver licenses.

HPC licenses can be used from 2020 R2 forward to conduct concurrent simulations of parametric variations. Each additional parametric variation will consume either one HPC Pack license or eight (individual) HPC licenses, and each parametric variation provides four included parallel cores, analogous to the four cores included with each solver license. This license model provides increased return on investment as you increase the number of parametric variations.

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