HPC applications, such as CFD, depend heavily on the applications’ ability to scale compute tasks efficiently in parallel across multiple compute resources. For detailed HPC-specific information, see visit the High Performance Computing page and download the CFD whitepaper, Computational Fluid Dynamics on AWS. Some of the most common concerns from CFD or HPC engineers are “how well will my application scale on AWS?”, “how do I optimize the associated costs for best performance of my application on AWS?”, “what are the best practices in setting up an HPC cluster on AWS to reduce the simulation turn-around time and maintain high efficiency?” This post aims to answer these concerns by defining and explaining important scalability-related parameters by illustrating the results from the CFD case. For a detailed case study describing TLG Aerospace’s experience and the results they achieved, see the TLG Aerospace case study.įor HPC workloads that use multiple nodes, the cluster setup including the network is at the heart of scalability concerns. TLG Aerospace, a Seattle-based aerospace engineering services company, contributed the data used in this blog. We also discuss the effects of scaling on efficiency, simulation turn-around time, and total simulation costs. This scenario demonstrates the scaling of an external aerodynamics CFD case with 97 million cells to over 4,000 cores of Amazon EC2 C5n.18xlarge instances using the Simcenter STAR-CCM+ software. In this blog, we define and demonstrate the scalability metrics for a typical real-world application using Computational Fluid Dynamics (CFD) software from Siemens, Simcenter STAR-CCM+, running on a High Performance Computing (HPC) cluster on Amazon Web Services (AWS). This post was contributed by Dnyanesh Digraskar, Senior Partner SA, High Performance Computing Linda Hedges, Principal SA, High Performance Computing
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