Tag: Scientific Computing

Today we’re announcing Research and Engineering Studio on AWS, a self-service portal to help scientists and engineers access and manage virtual desktops to see their data and run their interactive applications in the cloud.

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.

AWS ParallelCluster 3.7 now supports adding login nodes to your cluster, out of the box. Here, we’ll show you how to set this up, and highlight some important tunable options for tweaking the experience.

In this post we’ll show you how to deploy ParallelCluster in a shared VPC environment so you can separate infrastructure management, cluster operations, and help segregate costs, too.

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.

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

Lawrence Livermore National Laboratory (LLNL) and AWS are again joining forces to provide a training opportunity for emerging HPC tools and application. In this post you’ll find out the details of those tutorials, and find out how to participate.

Today, we’re announcing the availability of Spack configs for AWS ParallelCluster. You can use these configurations to install optimized HPC applications quickly and easily on your AWS-powered HPC clusters.

With #AWS #ParallelCluster 3.3, you can now easily take advantage of #EC2 On-Demand Capacity Reservations to help ensure your jobs have the capacity they need when they need it. This post describes the new feature and how you can benefit from it.

This is the second of a two-part post.Part 1 discussed the workflow for developing AI models using MATLAB for seismic interpretation. Today, we will discuss the various compute resources leveraged from AWS and NVIDIA for developing the models.