AWS HPC Blog

Today we’re showing you our community library of HPC Recipes for AWS. It’s a public repo @github that will help you achieve feature-rich, reliable HPC deployments ready to run your workloads no matter where you’re starting from.

In this post, we’ll show you an open-source solution for a real-time quant trading system that you can deploy on AWS. We’ll go over the challenges brought on by monitoring portfolios, the solution, and its components. We’ll finish with the installation and configuration process and show you how to use it.

In this post, we hear from Maxar’s WeatherDesk team on how they deploy their HPC workloads using a “fail fast” software development technique so they can be sure of meeting customer deadlines for their business.

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.

Today we’re announcing the integration between Amazon File Cache and AWS ParallelCluster – super important for hybrid scenarios. We’ll show you how it works and how to deploy it.

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 discuss the performance results we saw from the new hpc7a instance, running HPC workloads like CFD, molecular dynamics, and weather prediction codes.

In this post, we show how Reezocar uses computer vision to change the way they detect damage and price used vehicles for re-sale in secondary markets. This reduces landfill and helps achieve the goals of the circular economy.

In this post we’ll show how to use AWS Batch, AWS Fargate and Amazon Event Bridge to create a job scheduling solution for containers that’s fully managed, serverless, and event-driven.

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%!