Category: Best Practices

Check out our latest blog post to learn how AWS enables building secure, compliant high performance computing (HPC) environments aligned with NIST SP 800-223 guidelines. We walk through the key components, security considerations, and steps for deploying a zone-based HPC architecture on AWS.

This post was contributed by Eran Brown, Principal Engagement Manager, Prototyping Team, Vedanth Srinivasan, Head of Solutions, Engineering & Design, Edmund Chute, Specialist SA, Solution Builder, Priyanka Mahankali, Senior specialist SA, Emerging Domains For engineering companies, the cost of Computer Aided Design and Engineering (CAD/CAE) tools can as high as 20% of product development cost. […]

Optimizing workloads for performance and cost-effectiveness is crucial for businesses of all sizes – and especially helpful for workloads in the cloud, where there are a lot of levers you can pull to tune how things run. AWS offers a vast array of instance types in Amazon Elastic Compute Cloud (Amazon EC2) – each with […]

Managing extensive HPC workflows? This post details how to monitor resource consumption without compromising security. Check it out for a customizable reference architecture that sends only relevant data to your monitoring account.

EnginFrame Views users: check out this post on migration options to maintain secure remote access to your HPC environment. As AWS sunsets NICE EnginFrame, alternatives built on Amazon DCV can provide a seamless transition.

If you work in the semiconductor industry with electronic design automation tools and workflows, this guide will help you build an HPC cluster on AWS specifically configured for your needs. It covers AWS ParallelCluster and customizations specifically to cater to EDA.

If you want to build enterprise-grade HPC on AWS, what’s the best path to get started? Should you create a new AWS account and build from scratch? In this post we’ll walk you through the best practices for getting setup cleanly from the start.

Today we dive into details of AWS Batch fair share policies and show how they affect job placement. You’ll see the result of different share policies, and hear about practical use cases where you can benefit from fair share job queues in Batch.

Today we’re going to show you how you can automate cluster deployment and create self-documenting infrastructure at the same time, which leads to more repeatable results that are easier to manage (and replicate).

AWS Batch customers often ask for guidance to optimize their architectures and make their workload to scale rapidly. Here we describe an observability solution that provides insights into your AWS Batch architectures and allows you to optimize them for scale and quickly identify potential throughput bottlenecks for jobs and instances.