Introducing AWS HPC Connector for NICE EnginFrame

By Amazon Web Services

December 8, 2021

HPC customers regularly tell us about their excitement when they’re starting to use the cloud for the first time. In conversations, we always want to dig a bit deeper to find out how we can improve those initial experiences and deliver on the potential they see. Most often they’ve told us they need a simpler way to get started with migrating and bursting their workloads into the cloud.

Today we’re introducing AWS HPC Connector, which is a new feature in NICE EnginFrame that allows customers to leverage managed HPC resources on AWS. With this release, EnginFrame provides a unified interface for administrators to make hybrid HPC resources available both on-premises and within AWS. It means highly specialized users like scientists and engineers can use EnginFrame’s portal interface to run their important workflows without having to understand the detailed operation of infrastructure underneath. HPC is, after all, a tool used by humans. Their productivity is the real measure of success, and we think AWS HPC Connector will make a big difference to them.

In this post, we’ll provide some context around EnginFrame’s typical use cases, and show how you can use AWS HPC Connector to stand up HPC compute resources on AWS.

Background

NICE EnginFrame is an installable service-side application that provides a user-friendly application portal for HPC job submission, control, and monitoring. It includes sophisticated data management for every stage of a job’s lifetime, and integrates with HPC job schedulers and middleware tools to submit, monitor, and manage those jobs. The modular EnginFrame system allows for extreme customization to add new functionality (application integrations, authentication sources, license monitoring, and more) via the web portal.

The favorite feature for end users is EnginFrame’s web portal which provides an easy-to-understand, and consistent, user interface. The underlying HPC compute and storage can be used without needing to be fluent in either command line interfaces (CLIs), or in writing scripts. This frees you to scale your HPC systems underneath, and make them available to non-IT audiences who are focused on curing cancer or designing a better wind turbine.

Behind the scenes, EnginFrame “spools” a management process for each submitted job. This spooler runs in the background to manage data movement and job placement on the selected computational resource, and returns the results when the job finishes. This is transparent to the end user. As the administrator, you provide the necessary configuration to set up an application —app-specific parameters, location of data, where to run analysis, who can submit jobs. The admin portal also shows health and state information for the registered HPC systems, as shown in Figure 1.

Figure 1: NICE EnginFrame operational portal showing historical resource usage.

Prior to this release, EnginFrame treated all registered HPC clusters as the same, even if some were static on-premises resources, and others elastic clusters in the cloud. Specific to AWS, EnginFrame left all the decisions about your AWS infrastructure to you, including network layouts, security posture, and scaling. Quite often customers used AWS ParallelCluster (our cluster-management tool that makes it easy to deploy and manage HPC clusters on AWS) to stand up clusters within an AWS Region. They’d then manually install EnginFrame on their head node and integrate the two. While this approach worked, we knew the experience could be better.

In September, we introduced new API capabilities in ParallelCluster 3, in preparation for today, so you can have all the functionality of ParallelCluster in EnginFrame with a single administration, management, and deployment path for hybrid HPC.

AWS HPC Connector

AWS HPC Connector begins by letting you register ParallelCluster 3 configuration files in the EnginFrame admin portal. The ParallelCluster configuration file is designed as a simple YAML text file for describing the resources needed for your HPC applications and automating their provisioning in a secure manner. Once a ParallelCluster configuration is registered within EnginFrame, you can start and stop clusters as necessary. The cluster will scale the compute resources based on the number of submitted jobs, according to your defined scaling criteria and node types, up to the limits you set for running instances. Once the submitted jobs are complete, ParallelCluster is designed to automatically stop the compute instances it created, by scaling down to the minimum number of instances you defined, which is usually zero. At that point, only the head node remains running – ready to receive new jobs. Figure 2 has a high-level architecture diagram showing AWS HPC Connector in EnginFrame working in concert with ParallelCluster to stand up resources on AWS.

Figure 2: High-level architecture of NICE EnginFrame AWS HPC Connector.

Read the full blog to learn more about using NICE EnginFrame AWS HPC Connector to manage your workflows across on-premises and on AWS.

Reminder: You can learn a lot from AWS HPC engineers by subscribing to the HPC Tech Short YouTube channel, and following the AWS HPC Blog channel.

 

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