For HPC workloads that use multiple nodes, the cluster setup including the network is at the heart of scalability concerns. 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?”.
The new blog post by AWS aims to answer these concerns by defining and explaining important scalability-related parameters by illustrating the results from the CFD case. The authors of the blog 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 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.
Read the new blog here and learn about
- the scalability of a commercial CFD software Simcenter STAR-CCM+ for an external aerodynamics simulation performed on the Amazon EC2 C5n.18xlarge instances
- the effects of scaling on efficiency, simulation turn-around time, and total simulation costs.
- how availability of EFA, a high-performing network device on these instances results in excellent scalability of the application
- the case turn-around time and associated costs of running Simcenter STAR-CCM+ on AWS hardware
- importance of throughput and availability for HPC applications
Click to read full article here.
The data used in the article was contributed by TLG Aerospace, a Seattle-based aerospace engineering services company.