AWS HPC Blog

As a part of AWS’s “Digital Technologies for a Circular Economy” initiative, we joined forces with Accenture, Intel and Good Chemistry to massively scale quantum chemistry simulations. This is the first and most complex step to discovering new pathways for PFAS destruction for a cleaner world.

In this post, we describe an integration of Open OnDemand with AWS ParallelCluster so admins can provide web-based access to HPC resources beyond what they have at their site, by using the AWS cloud to add new capabilities and extend capacity.

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.

Slurm accounting adds flexibility, transparency, and control to operating an #HPC cluster. #AWS #ParallelCluster 3.3.0 can now automatically configure #Slurm accounting whether you are using your own database or Amazon #Aurora.

In this blog post, we benchmark the performance of Sentieon’s DNAseq and DNAscope pipelines using publicly available genomics datasets on AWS. You will gain an understanding of the runtime, cost, and accuracy performance of these germline variant calling pipelines across a wide range of Amazon EC2 instances.

AWS Batch customers often ask for guidance to optimize their architectures and make their workload to scale rapidly. Here we describe an observability solution that provides insights into your AWS Batch architectures and allows you to optimize them for scale and quickly identify potential throughput bottlenecks for jobs and instances.

In this post we recap all the really significant feature released in DCV from 2022 that delighted our customers. Of course, we’re still not done, so expect more in 2023.

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.

In this blog post, we discuss how geoscientists can use shallow RNN-based algorithms with MATLAB to automatically recognize distinct geologic features in seismic images. We discuss the workflow for developing the AI models using MATLAB for seismic interpretation.  In a second post will introduce the various compute resources leveraged from AWS and NVIDIA for developing the models.