Tag: AWS Batch

This post was contributed by Orang Vahid (Dir of Engineering Services) and Kayla Rossi (Application Engineer) at Maplesoft, and Ross Pivovar (Solution Architect) and Adam Rasheed (Snr Manager) from Autonomous Computing at AWS One of the most common objectives for our Digital Twin (DT) customers is to use DTs for scenario analysis to assess risk […]

AWS is developing new tools that enable easier and faster deployment of level 3/4 digital twins. This post discusses how a fleet calibrated level 3 digital twin can be cost effectively deployed on AWS Cloud.

In this blog post, we’ll explore how Green Urban Scenarios simulator (GUS) helps urban planners explore the impact of green infrastructure on the urban environment using digital twins and simulations scaled using AWS Batch.

Digital twins can be hard if they deviate from real-world behavior as real systems degrade and change over time. Today we’ll show digital twins that calibrate on operational data, using TwinFlow on AWS.

Today, we’re diving deep into the open-source frameworks that move MPI messages around, and showing you how work we did in the Open MPI and libfabrics community lead to an improvement for EFA users – and everyone else, too.

In this post we’ll show how generative AI, combined with conventional physics-based CFD can create a rapid design process to explore new design concepts in automotive and aerospace from just a single image.

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