AWS HPC Blog

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 […]

Elevate your mining and construction business to new heights with the power of 3D world modeling and AI-driven scene interpretation. Our latest blog post delves into the underlying technologies, from SLAM and photogrammetry to point-cloud analysis, and demonstrates how to scale the execution of these compute-intensive algorithms on AWS. Unlock the potential to optimize your workflows, improve decision-making, and drive sustainable growth.

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.

Agentic AI is revolutionizing computational fluid dynamics (CFD) simulations, enabling experienced engineers to focus on physics, innovation, and engineering judgment rather than tedious coding and debugging. Our latest blog explores how this transformative technology can help your team deliver complex projects more rapidly while maintaining scientific rigor.

This post was contributed by Mohamed K. Elshazly, PhD, Kareem Abdol-Hamid, Sam Bydlon, PhD, Aarabhi Achanta, and Mark Azadpour The decarbonization of our modern economy depends on solving a defining scientific challenge: developing batteries that are both safe and high performing. From electrical grids to vehicles and aviation, these energy storage devices must provide power […]