AWS HPC Blog

Category: Customer Solutions

How Amazon’s Search M5 team optimizes compute resources and cost with fair-share scheduling on AWS Batch

How Amazon’s Search M5 team optimizes compute resources and cost with fair-share scheduling on AWS Batch

In this post, we share how Amazon Search optimizes their use of accelerated compute resources using AWS Batch fair-share scheduling to schedule distributed deep learning workloads.

Improving NFL player health using machine learning with AWS Batch

Improving NFL player health using machine learning with AWS Batch

In this post we’ll show you how the NFL used AWS to scale their ML workloads and produce the first comprehensive dataset of helmet impacts across multiple NFL seasons. They were able to reduce manual labor by 90% and the results beats human labelers in accuracy by 12%!

Streamlining distributed ML workflow orchestration using Covalent with AWS Batch

Streamlining distributed ML workflow orchestration using Covalent with AWS Batch

Complicated multi-step workflows can be challenging to deploy, especially when using a variety of high-compute resources. Covalent is an open-source orchestration tool that streamlines the deployment of distributed workloads on AWS resources. In this post, we outline key concepts in Covalent and develop a machine learning workflow for AWS Batch in just a handful of steps.

Building a 4x faster and scalable algorithm using AWS Batch for Amazon Logistics

Building a 4x faster and more scalable algorithm using AWS Batch for Amazon Logistics

In this post, AWS Professional Services highlights how they helped data scientists from Amazon Logistics rearchitect their algorithm for improving the efficiency of their supply-chain by making better planning decisions. Leveraging best practices for deploying scalable HPC applications on AWS, the teams saw a 4X improvement in run time.

Running accurate, comprehensive, and efficient genomics workflows on AWS using Illumina DRAGEN v4.0

In this blog, we provide a walkthrough of running Illumina DRAGEN v4.0 genomic analysis pipelines on AWS, showing accuracy and efficiency, copy number analysis, structural variants, SMN callers, repeat expansion detection, and pharmacogenomics insights for complex genes. We also highlight some benchmarking results for runtime, cost, and concordance from the Illumina DRAGEN DNA sequencing pipeline.

Massively-scaling quantum chemistry to support a circular economy

Massively-scaling quantum chemistry to support a circular economy

As a part of AWS’s “Digital Technologies for a Circular Economy” initiative, we joined forces with Accenture, Intel and Good Chemistry to massively scale quantum chemistry simulations. This is the first and most complex step to discovering new pathways for PFAS destruction for a cleaner world.

Cost-effective and accurate genomics analysis with Sentieon on AWS

In this blog post, we benchmark the performance of Sentieon’s DNAseq and DNAscope pipelines using publicly available genomics datasets on AWS. You will gain an understanding of the runtime, cost, and accuracy performance of these germline variant calling pipelines across a wide range of Amazon EC2 instances.

Accelerating Genomics Pipelines Using Intel’s Open Omics Acceleration Framework on AWS

In this blog, we showcase the first version of Open Omics and benchmark three applications that are used in processing NGS data – sequence alignment tools BWA-MEM, minimap2, and single cell ATAC-Seq on Xeon-based Amazon Elastic Compute Cloud (Amazon EC2) Instances.