AWS HPC Blog

Austin Cherian

Author: Austin Cherian

Austin is a Senior Product Manager-Technical for High Performance Computing at AWS. Previously, he was a Snr Developer Advocate for HPC & Batch, based in Singapore. He's responsible for ensuring AWS ParallelCluster grows to ensure a smooth journey for customers deploying their HPC workloads on AWS. Prior to AWS, Austin was the Head of Intel’s HPC & AI business for India where he led the team that helped customers with a path to High Performance Computing on Intel architectures.

Discontinuation of NICE EnginFrame effective September 25th, 2025

After careful consideration, we have made the decision to discontinue NICE EnginFrame including NICE EnginFrame views, effective September 25, 2025. If you want to continue using NICE EnginFrame beyond the end-of-support date, we recommend contacting NI-SP, an AWS partner with decades of experience implementing and supporting NICE EnginFrame for enterprises.

Enhancing ML workflows with AWS ParallelCluster and Amazon EC2 Capacity Blocks for ML

Enhancing ML workflows with AWS ParallelCluster and Amazon EC2 Capacity Blocks for ML

No more guessing if GPU capacity will be available when you launch ML jobs! EC2 Capacity Blocks for ML let you lock in GPU reservations so you can start tasks on time. Learn how to integrate Caacity Blocks into AWS ParallelCluster to optimize your workflow in our latest technical blog post.

GROMACS performance on Amazon EC2 with Intel Ice Lake processors

We recently launched two new Amazon EC2 instance families based on Intel’s Ice Lake – the C6i and M6i. These instances provide higher core counts and take advantage of generational performance improvements on Intel’s Xeon scalable processor family architectures. In this post we show how GROMACS performs on these new instance families. We use similar methodologies as for previous posts where we characterized price-performance for CPU-only and GPU instances (Part 1, Part 2, Part 3), providing instance recommendations for different workload sizes.

Introducing AWS ParallelCluster multiuser support via Active Directory

Today we’re announcing the release of AWS ParallelCluster 3.1 which now supports multiuser authentication based on Active Directory (AD). Starting with v3.1.1 clusters can be configured to use an AD domain managed via one of the AWS Directory Service options like Simple AD or AWS Managed Microsoft AD (MSAD). This blog post describes the new feature, and gives an example of a configuration block for ParallelCluster 3 configuration files.

Deep dive into the AWS ParallelCluster 3 configuration file

In September, we announced the release of AWS ParallelCluster 3, a major release with lots of changes and new features. To help get you started migrating your clusters, we provided the Moving from AWS ParallelCluster 2.x to 3.x guide. We know moving versions can be a quite an undertaking, so we’re augmenting that official documentation with additional color and context on a few key areas. With this blog post, we’ll focus on the configuration file format changes for ParallelCluster 3, and how they map back to the same configuration sections for ParallelCluster 2.

Figure 4: Relative price-to-performance ratio ($USD/ns) while scaling the simulation across single and multi-GPU instances and comparing to CPU (EFA enabled) performance-to-price (baseline CPU perf).

Running GROMACS on GPU instances: multi-node price-performance

This three-part series of posts cover the price performance characteristics of running GROMACS on Amazon Elastic Compute Cloud (Amazon EC2) GPU instances. Part 1 covered some background no GROMACS and how it utilizes GPUs for acceleration. Part 2 covered the price performance of GROMACS on a particular GPU instance family running on a single instance. […]