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Scaling ANSYS Related Workloads to Increase Performance

Manufacturers are under intense pressure to create and introduce new products on a consistent basis in order to remain competitive. Those that can conceive, develop, test and bring products to market quickly stand to realize improvements to overall business performance and profitability.

Computer-aided engineering (CAE) streamlines the product development process and drives faster time-to-market by helping manufacturers resolve design challenges, forecast real world product performance and test fewer prototypes.


Best-of-breed CAE software like ANSYS can nurture design innovation and enable faster delivery of more successful product offerings, but only if IT can scale to support a wide range of CAE applications and workloads. As engineering simulations become increasingly realistic and complex, higher demands are placed on IT systems making hardware capacity beyond a single workstation or server required to reduce runtimes and support larger model sizes. Legacy systems lacking in flexibility and scalability will only serve to disrupt productivity as engineers push workstations to their limits.

The task of scaling up compute capacity, memory and data storage to support ANSYS workloads falls on the shoulders of IT. Workload optimization powered by high performance computing (HPC) technology can help manufacturers accelerate IT transformation and raise efficiencies to product development processes.

Many ANSYS environments must be accessed and shared by a geographically distributed workforce, working simultaneously and requiring ways to seamlessly collaborate. Centralizing computing resources can help control costs, bolster data security, and improve the management of multiple workstations while offering remote visualization capabilities with greater performance and flexibility.

Adding clusters to HPC infrastructure is another technique which allows engineers to increase design productivity, significantly reduce wait times and develop models as large and complex as necessary. Clustering also simplifies user access to workstations using the latest HPC technology controlled from a central location.

Scaling to support ANSYS-related workloads can enable manufacturers to achieve operational goals including meeting client-defined solution requirements, supporting sizeable CAE workloads, and increasing collaboration by strengthening remote access capabilities. As CAE models generate faster and with less downtime, product development processes are shortened, allowing companies to focus on activities that foster business growth, like increasing the size of their engineering workforce.

The investment it takes to scale up can present financial challenges for many manufacturers, particularly for small and medium enterprises. However, research has shown that the cost savings offered by HPC in the form of improvements to engineering productivity and product quality can quickly offset additional investments in HPC technology. According to IDC, approximately $43 of profit/cost savings is gained per dollar of investment in HPC.

Data-driven organizations that realize the necessity of scaling up can start the transformation process by evaluating current ANSYS workloads, and identifying current hardware infrastructure as well as available resources. Computing power needed for business-critical computations and simulations will guide hardware and software decisions, and trusted compute partners can assist organizations in mixing and matching the right hardware solution to meet their simulation requirements.

The adoption of HPC solutions makes it possible to improve engineering productivity and performance while diminishing infrastructure complexity. Gaining the flexibility and scalability to support ever-evolving simulation workloads is critical as manufacturers strive to realize efficiencies on their way to converting their most innovative ideas into business value.