Deploy a multiuser environment for computationally intensive workflows, such as Computer-Aided Engineering (CAE)
Publication date: November 2019 (last update: June 2022)
Amazon Web Services (AWS) allows data scientists, designers, and engineers to run their scale-out workloads such as those that require parallel processing and deep learning training, without having extensive cloud experience.
The Scale-Out Computing on AWS solution helps customers easily deploy and operate a multiuser environment for computationally intensive workflows such as Computer-Aided Engineering (CAE). The solution features a large selection of compute resources, a fast network backbone, unlimited storage, and budget and cost management directly integrated within AWS. This solution also deploys a user interface (UI) with cloud workstations, file management, and automation tools that allow you to create your own queues, scheduler resources, Amazon Machine Images (AMIs), and management functions for user and group permissions.
This solution is designed to be a production-ready reference implementation you can use as a starting point for deploying an AWS environment to run scale-out workloads, allowing users to focus on running simulations designed to solve complex computational problems. For example, with the unlimited storage capacity provided by Amazon Elastic File System
This implementation guide describes architectural considerations and configuration steps for
deploying Scale-Out Computing on AWS in the AWS Cloud. It includes links to an AWS CloudFormation
The guide is intended for IT infrastructure architects, administrators, and DevOps professionals who have practical experience architecting in the AWS Cloud.
