Tag: Scientific Computing

Interested in eliminating friction between data science and engineering teams? Read this post to learn how HAQM successfully transitioned Jupyter Notebooks from the lab to production.

Balancing fixed budgets with fluctuating HPC needs is challenging. Discover a customizable solution for automatically setting weekly resource limits based on previous spending.

Interested in performing high-accuracy computational chemistry simulations faster? Check out this new post about Promethium, a solution from QC Ware that leverages AWS to accelerate simulations by up to 100x.

In this second part of using Nextflow for machine learning for life science workloads, we provide a step-by-step guide, explaining how you can easily deploy a Seqera environment on AWS to run ML and other pipelines.

New OS-level checkpointing tools can let you run existing HPC codes on EC2 Spot instances with minimal impact from interruptions. Read on for the details.

Looking to integrate AWS ParallelCluster into an automated workflow? This post shows how to submit and monitor jobs programmatically with Slurm REST API (code examples included).

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.