Tag: Research Computing

This blog was co-authored by Takehiro Nakajima and Mark Azadpour from AWS and Rintaro Yamada, Rei Kajitani and Ryo Kunimoto from Daiichi Sankyo In recent years, the informatics field of drug discovery has seen a rapid increase in workloads requiring large-scale parallel computing, such as genome analysis, structure prediction, and drug design. Daiichi Sankyo has […]

AWS re:Invent 2025 returns to Las Vegas, Nevada on December 1, uniting AWS builders, customers, partners, and IT professionals from across the globe. This year’s event offers you exclusive access to compelling customer stories and insights from AWS leadership as they tackle today’s most critical challenges in high-performance computing, from accelerating scientific discovery to optimizing […]

This post was contributed by Ross Pivovar, Rafał Ołdziejewski, Cindy Xin Qi Lee Offshore wind farms play a critical role in the global transition to renewable energy and clean power generation. But generating electricity is only half the battle—safely and efficiently transporting that power to the grid through undersea cables is equally important. Today, we’ll […]

It’s been a busy month in the world of HPC on AWS: we’ve seen new data sets, refinements to cluster operations, and deeper thinking about how workloads map to infrastructure. For our customers driving R&D with HPC, those changes matter (and yes, the nerd in me is quietly excited). In today’s post, we’ll tell you […]

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 […]

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 […]

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 […]

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 […]

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 […]

Discover how a new simulation-based approach using agent-based modeling, rich data, and advanced analytics can help you build resilient supply chains that withstand disruptions. Learn more in our latest post.