Tag: AWS ParallelCluster

AWS is investing in researcher training with new learning plans for HPC, quantum, stats, AI/ML & generative AI. Check out the details!

This post was contributed by Eran Brown, Principal Engagement Manager, Prototyping Team, Vedanth Srinivasan, Head of Solutions, Engineering & Design, Edmund Chute, Specialist SA, Solution Builder, Priyanka Mahankali, Senior specialist SA, Emerging Domains For engineering companies, the cost of Computer Aided Design and Engineering (CAD/CAE) tools can as high as 20% of product development cost. […]

This post was contributed by Jeremiah Habets, Ross Pivovar, Pramod Daya, Pallavi Chari, and Adam Rasheed from AWS Customers tell us that they’re increasingly seeking holistic digital twin solutions spanning IoT, spatial computing, and predictive modeling domains. Integrating these diverse technical stacks presents challenges for builders. In a prior post, we described a four-level Digital […]

EnginFrame Views users: check out this post on migration options to maintain secure remote access to your HPC environment. As AWS sunsets NICE EnginFrame, alternatives built on Amazon DCV can provide a seamless transition.

How might AI change the rules of the energy game? A new post explores using large language models to power smarter, more adaptive agents in an energy supply chain simulation. Learn how LLMs could enable more nuanced decision-making behaviors.

Check out this new post to learn about identity options for Research and Engineering Studio on AWS. Understanding choices for SAML IdPs and Active Directory will help you plan secure VDI access.

This post was contributed by Gerard Gorman from Devito, and Cyril Lagrange, Gilles Tourpe, and Theo Wu from AWS The AWS Graviton4 processor represents a significant leap forward, with 96 Neoverse V2 cores and an enhanced memory subsystem. The 12 DDR5-5600 channels provide up to 75% more memory bandwidth than Graviton3 which is beneficial for […]

How can LLMs take agent-based simulation to the next level? Check out our new post on leveraging large language models’ capabilities for more realistic modeling.

Our team engineered some Open MPI optimizations for EFA to enhance performance of HPC codes running in the cloud. By improving MPI_AllReduce they improved scaling – matching commercial MPIs. Tests show gains for apps including Code Saturne and OpenFOAM on both Arm64 and x86 instances. Check out how these tweaks can speed up your HPC workloads in the cloud.

In this post, we’ll share two ways customers can operate HPC workloads using AWS ParallelCluster while completely isolated from the Internet. ParallelCluster supports many different network configurations to support a range of uses. When referring to isolation we mean situations where your HPC cluster is completely self-contained inside AWS, or where you have a private […]