Category: Compute

In life sciences research, managing large-scale computational resources and data efficiently is important for success. However, traditional on-premises environments often struggle to meet these requirements effectively. This post demonstrates how JSR Corporation transformed their research infrastructure using AWS ParallelCluster and AWS DataSync, achieving a 33% reduction in CPU usage and 85% in storage requirements. JSR’s […]

AstraZeneca’s genomic research requires extensive computational resources to analyze DNA sequences for developing life-saving therapies. As cloud infrastructure evolves with more powerful capabilities, customers can adopt them to see performance and efficiency gains. AstraZeneca successfully migrated to Amazon EC2 F2 instances for genomics, boosting performance by 60% and slashing costs by 70%.

In this post, learn how Adobe scaled AI inference for 29B+ assets across Adobe Firefly and Adobe Stock using AWS Batch and GPU-accelerated EC2, optimizing cost and performance at massive scale.

Are you running genomic workflows with Nextflow on AWS Batch and experiencing bottlenecks when staging large reference files? In this post, we will show you how to optimize your workflow performance by leveraging Mountpoint for Amazon S3 to stream reference data directly into your Nextflow processes, eliminating the need to stage large static files repeatedly.

This post was contributed by Ameya Kamerkar (Rivian), Vikram Pendyam (Rivian), Abhishek Chauhan (Rivian), Ajay Paknikar (AWS), Sandeep Sovani (AWS) Figure 1. Rivian’s custom Amazon Electric Delivery Vehicle (EDV) (Credits: Rivian media kit) In this post, we share how Rivian, a leading electric vehicle manufacturer, revolutionized their engineering simulation capabilities by migrating to AWS and […]

Running NVIDIA Cosmos world foundation models on AWS provides powerful physical AI capabilities at scale. This blog covers two production-ready architectures, each optimized for different organizational needs and constraints.

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 […]

We want to empower every scientist and engineer to solve hard problems by giving them access to the compute and analytical tools they need, when they need them. Cloud HPC can be a real human progress catalyst. If you run large scale simulations, tune complex models, or support researchers who consistently need more compute, the […]

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 […]

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 […]