AWS HPC Blog

Tag: Scientific Computing

What’s the difference between AWS ParallelCluster and AWS Parallel Computing Service?

It’s been a year since we announced AWS Parallel Computing Service (PCS). In a way this is the third generation of Slurm-based HPC orchestrators that we’ve brought to you. We’ve learned much from helping customers deploy serious production workloads on AWS ParallelCluster, which itself grew from the foundations layed by CfnCluster – the open-source project […]

A scientific approach to workload-aware computing on AWS

A scientific approach to workload-aware computing on AWS

HPC workloads demonstrate predictable resource patterns that can directly determine optimal cloud instance selection. To save you conducting extensive custom benchmarking, this blog post presents a data-driven methodology for instance selection based on established performance research. In this post, you’ll learn how to use coupling patterns to drive instance selection. We’ll outlines our scientific methodology […]

Dataset of protein-ligand complexes now available in the Registry of Open Data on AWS

by Deva Priyakumar, Beryl Rabindran, Alex Iankoulski, Prathit Chatterjee, Rakesh Srivastava, Ramanathan Sethuraman, Vladimir Aladinskiy, and Yusong Wang on in High Performance Computing Permalink Share

This post was contributed by U. Deva Priyakumar, Rakesh Srivatsava, Prathit Chatterjee, Vladimir Aladinskiy, Ramanathan Sethuraman, Yusong Wang, Alex Iankoulski, and Beryl Rabindran Today, we’re excited to announce the release of a comprehensive dataset featuring molecular dynamics (MD) trajectories for over 16,000 protein-ligand complexes (PLCs). This dataset, now available on AWS as part of the […]

Announcing expanded support for Custom Slurm Settings in AWS Parallel Computing Service.png

Announcing expanded support for Custom Slurm Settings in AWS Parallel Computing Service

Today we’re excited to announce expanded support for custom Slurm settings in AWS Parallel Computing Service (PCS). With this launch, PCS now enables you to configure over 65 Slurm parameters. And for the first time, you can also apply custom settings to queue resources, giving you partition-specific control over scheduling behavior. This release responds directly […]

Announcing Capacity Blocks support for AWS Parallel Computing Service

Announcing Capacity Blocks support for AWS Parallel Computing Service

This post was contributed by by Kareem Abdol-Hamid, Kyle Bush Today we’re happy to announce that support for Amazon EC2 Capacity Blocks for Machine Learning are now supported in AWS Parallel Computing Service (AWS PCS). This allows you to reserve and schedule GPU-accelerated Amazon EC2 instances for future use. That includes the NVIDIA Hopper GPU […]

Predict the unpredictable: Disrupting drug lead optimization using quantum mechanics simulation in the cloud

Quantum mechanics meets drug discovery: QSimulate’s latest advancements in QM-based FEP simulation are poised to transform the industry. Our blog post takes you on a journey through the groundbreaking science and innovative software that are redefining the future of drug design.

How Caris Life Sciences processed 400,000 RNAseq samples in 2.5 days with AWS Batch

How Caris Life Sciences processed 400,000 RNAseq samples in 2.5 days with AWS Batch

In the race to deliver precision medicine, time is of the essence. Caris Life Sciences, a pioneer in this field, leveraged AWS Batch to build a highly scalable solution that processed hundreds of thousands of genomic samples in record time. Discover how they achieved this remarkable feat and the key services that powered their breakthrough.