Tag: Amazon FSx for Lustre

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 AWS ParallelCluster 3.4, you can now build HPC clusters that span multiple Amazon EC2 Availability Zones. In this post, we describe how the new feature works, how to use it, and some implications for cluster design that it raises.

Since launch, EFA has seen continuous improvements in performance. In this post, we talk about our 2nd generation of EFA, which takes another step in improving Machine Learning and High Performance Computing in the Cloud.

AWS ParallelCluster version 3.2 introduces support for two new Amazon FSx filesystem types (NetApp ONTAP and OpenZFS). It also lifts the limit on the number of filesystem mounts you can have on your cluster. We’ll show you how, and help you with the details for getting this going right away.

In this blog post, we’ll walk you through the process of building a successful Cryo-EM benchmarking pilot using AWS ParallelCluster, Amazon FSx for Lustre, and cryoSPARC (from Structura Biotechnology) and explain some of our design decisions along the way.

Batch processing is a common need across varied machine learning use cases such as video production, financial modeling, drug discovery, or genomic research. The elasticity of the cloud provides efficient ways to scale and simplify batch processing workloads while cutting costs. In this post, you’ll learn a scalable and cost-effective approach to configure AWS Batch Array jobs to process datasets that are stored on Amazon S3 and presented to compute instances with Amazon FSx for Lustre.