AWS HPC Blog
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Create a Slurm cluster for semiconductor design with AWS ParallelCluster
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.
How Amazon’s Search M5 team optimizes compute resources and cost with fair-share scheduling on AWS Batch
In this post, we share how Amazon Search optimizes their use of accelerated compute resources using AWS Batch fair-share scheduling to schedule distributed deep learning workloads.
The plumbing: best-practice infrastructure to facilitate HPC on AWS
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.
Deep-dive into Hpc7a, the newest AMD-powered member of the HPC instance family
Today we discuss the performance results we saw from the new hpc7a instance, running HPC workloads like CFD, molecular dynamics, and weather prediction codes.
Improving NFL player health using machine learning with AWS Batch
In this post we’ll show you how the NFL used AWS to scale their ML workloads and produce the first comprehensive dataset of helmet impacts across multiple NFL seasons. They were able to reduce manual labor by 90% and the results beats human labelers in accuracy by 12%!
Diving Deeper into Fair-Share Scheduling in AWS Batch
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.
Automate your clusters by creating self-documenting HPC with AWS ParallelCluster
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).
Deploying predictive models and simulations at scale using TwinFlow on AWS
AWS TwinFlow is an open source framework to build and deploy predictive models using heterogenous compute pipelines on AWS. In this post, we show the versatility of the framework with examples of engineering design, scenario analysis, systems analysis, and digital twins.
Streamlining distributed ML workflow orchestration using Covalent with AWS Batch
Complicated multi-step workflows can be challenging to deploy, especially when using a variety of high-compute resources. Covalent is an open-source orchestration tool that streamlines the deployment of distributed workloads on AWS resources. In this post, we outline key concepts in Covalent and develop a machine learning workflow for AWS Batch in just a handful of steps.
Explore costs of AWS Batch jobs run on Amazon EKS using pod labels and Kubecost
Today we show you how to get insights into the costs of running AWS Batch workloads on Amazon EKS using Kubernetes pod labels with Kubecost.