This Guidance shows how you can build and run large-scale spatial simulations on AWS without having to manage the underlying compute, memory, or networking infrastructure. It uses AWS SimSpace Weaver, a fully managed service that lets you create complex and expansive simulation environments at scale. You specify the compute capacity needed for the simulation and define how it should be partitioned. While the spatial simulations are running, you can view the simulations in real-time, make modifications, or interact with the simulations to change the behavior of the dynamic entities and improve the representation of your unique scenarios.  

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Architecture Diagram

[Architecture diagram description]

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Well-Architected Pillars

The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

  • During the implementation of this Guidance, you might encounter issues with launching or running SimSpace Weaver on the backend Cloud infrastructure; in order to understand the cause of the issue, you can utilize the local mode of SimSpace Weaver. The local mode allows diagnosis on a reduced number of partitions and verbose information about the underlying issue without invoking SimSpace Weaver runtime. If there are further issues with the service, create a support case in the AWS Management Console; our team will investigate the nature of the incident and respond accordingly.

    Read the Operational Excellence whitepaper 
  • For the backend Amazon EC2 c5.24xlarge instances, the AWS Identity and Access Management (IAM) role is attached to these prior to launching the simulation—giving minimal access to resources, such as Amazon CloudWatch for logging and Amazon S3 for recording data. Authentication is enabled to connect the front-end client to the rendering G4dn or G5dn node using the NICE DCV protocol or streaming service, helping to ensure that resources are protected.

    Read the Security whitepaper 
  • While the SimSpace Weaver backend architecture is designed to be resilient, there are limitations on the number of concurrently connected users or clients for each simulation due to bandwidth constraints. Additionally, when using the rendering node, if the local connection is not stable it might result in jitter and other issues when viewing entities within Unreal or Unity simulation engines. This can be mitigated by using a higher bandwidth connection from the remote clients to the AWS Cloud.

    Read the Reliability whitepaper 
  • The Amazon EC2 C5 Instances are selected for the backend infrastructure due to the compute heavy nature of spatial simulations. The G4dn and G5dn rendering nodes are selected due to the heavy GPU requirements of visualization engines, such as Unreal and Unity, particularly with detailed meshes and graphics. NICE DCV is chosen to stream the rendering due to the high-performance remote desktop streaming capabilities and underlying compression technologies.

    Read the Performance Efficiency whitepaper 
  • As part of the simulation schema, the underlying backend resources are specified in terms of the number of c5.24xlarge compute nodes, together with the grid placement of spatial partitions within those nodes. Prior to the simulation, the minimum resources needed to run the simulation are specified accordingly. For the front-end architecture, up to 10 clients can simultaneously connect to the SimSpace Weaver simulation by using the TCP channel specified in the corresponding View Apps. This number can be reduced by connecting with fewer clients and using fewer View Apps as specified in the schema in order to maximize the available bandwidth and compute while minimizing cost.

    Read the Cost Optimization whitepaper 
  • This Guidance and the underlying technology behind SimSpace Weaver leverages data replication for entity information and subscriptions for streaming minimal information. This occurs from the Cloud spatial simulation to the visualization engine (such as Unreal) running on the rendering node to ensure that there are no bandwidth bottlenecks in data access. Because of the improved efficiency in computing, storage, and networking, fewer inconsistent simulation runs reduces waste. Additionally, with the snapshot feature, the state of the entire simulation can be recorded on Amazon S3 and then accessed once the simulation has finished. Therefore, there is no need to continuously connect to the simulation for data collection, reducing computational burden while supporting a sustainable environment.

    Read the Sustainability whitepaper 

Implementation Resources

A detailed guide is provided to experiment and use within your AWS account. Each stage of building the Guidance, including deployment, usage, and cleanup, is examined to prepare it for deployment.

The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.

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This [blog post/e-book/Guidance/sample code] demonstrates how [insert short description].


The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.

References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.

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