Category: Compute

This post was contributed by Kirill Bogdanov (Pr. Solutions Architect at AWS) and Alan Parry, (CTO at YellowDog). Large-scale compute grids sit at the heart of modern financial services operations. They power overnight batch runs for regulatory risk and prepare traders for the coming day. During trading, the same grids drive intraday ‘value at risk’ […]

This post was contributed by Stefan Dittforth and Michael Mueller Introduction Oxford Nanopore Technologies (ONT) sequencing enhances genome analysis in research and healthcare with its ability to produce long-read sequencing data in real-time. Long reads improve our ability to detect structural variation, resolve repetitive regions, perform haplotype phasing and analyze full-length transcripts, providing a more […]

Research teams moving from on-premises HPC environments often struggle with the complexity of cloud deployment. Traditional approaches require deep expertise in AWS networking, storage architectures, and Slurm configuration management. A typical manual deployment involves weeks of infrastructure provisioning, network topology design, scheduler configuration, and performance tuning. Research teams with limited platform engineering resources find themselves […]

High performance computing (HPC) workloads continue to grow in scale and complexity. Whether simulating airflow over an aircraft wing, modeling structural behavior under load, or performing crash simulation and multi-physics analysis, these workloads demand sustained compute throughput, high memory bandwidth, and efficient scaling across large clusters. Improvements in any one of these dimensions can reduce […]

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.