Computer Aided Engineering

TLG Aerospace reduces computational fluid dynamics (CFD) simulation costs by 75 percent, scales aerodynamic simulations to more than a thousand cores, and gained online simulation metrics by using AWS.
Using AWS, Autodesk can scale the use of generative design to run hundreds of simulations in one hour instead of several hours or days. Autodesk develops software for the engineering, design, and entertainment industries.
CADFEM uses AWS to make complex simulation software more accessible to smaller engineering firms, helping them compete with much larger ones.
By using AWS and providing users a flexible SaaS subscription model, OnScale offers engineers across disciplines nearly infinite HPC compute resources to run complex simulations, for a fraction of the time it’d take and cost to run locally.
AWS allowed Cadence to isolate workloads from one another and ensure users and applications didn’t compete for resources. The company was able to run faster, and significantly reduce job times.
Western Digital built a cloud-scale HPC cluster on AWS and they were able to run 2.3 million simulation jobs on a single HPC cluster of 1 million vCPUs – built using Amazon EC2 Spot Instances, reducing the time to results from 20 Days to 8 hours.
Volkswagen Group Research sought to improve its simulation and design cycles and chose to run a PoC on AWS using Altair ultraFluidX. Following a successful PoC, Altair estimates Volkswagen could save up to 70 percent of its current hardware cost using ultraFluidX.
ProteanTecs uses AWS to get the flexibility and scalability they needed to support high-performance computing (HPC) workloads that run millions of simulations each day.
Innovium used AWS to remove the compute scalability barrier, so that they can focus exclusively on product innovations.
Executing HPC simulations on AWS infrastructure allows Western Digital to produce a higher quality product and achieve a faster time-to-market.
The scalability provided by AWS and the ease of implementation provided by CFD Direct offer Big Ass Fans an efficient and cost-effective solution to respond to customer requirement instantly.
By using an HPC solution on AWS, Avio Aero is no longer forced to leave customers waiting for results because of limited on-premises resources.

Healthcare and Life Sciences


Illumina uses AWS to globally scale its DNA sequencing technologies while driving down costs by 100X and meeting the security requirements of different countries and customers.

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Learn how Bristol-Myers Squibb was able to perform intensive clinical trial simulations on AWS with a 98% time savings, which helps them to shorten study time and reduce patient impact.

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Learn how Thermo Fisher used AWS to build the Thermo Fisher Cloud, a platform that helps medical researchers and scientists securely store, analyze, and share data globally. 

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Learn how DNAnexus built a platform for genomic analysis on AWS to meet the demanding requirements of HIPAA, CAP/CLIA, GxP, and other privacy laws and regulations.

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Learn how Celgene enables secure collaboration between its own researchers and academic research labs using AWS.

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Learn how Astra Zeneca was able to process 20,000 exomes of genomic data in just 20 days using AWS high-performance computing resources.

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Fabric Genomics uses AWS to process whole genomes in minutes, help physicians diagnose diseases faster, and scale to support large genomic datasets.

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OpenEye Scientific Software uses AWS to save $800,000 yearly, help pharmaceutical companies deploy thousands of processing cores in weeks instead of months, and improve collaboration.

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Metabiota’s uses Rescale platform on AWS to manage their pandemic influenza event catalog that requires 18 million simulations and produces 100 TB of uncompressed data.  

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Using AWS, Igenomix has the on-demand capacity to run highly data-intensive next-generation sequencing (NGS) workloads, so it can analyze 1,150 percent more samples.

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Learn how GENALICE processed genomes from 800 Alzheimer’s disease patients in just 60 minutes, which would have taken its competitor more than two weeks to complete.  

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Using AWS has helped enable Seven Bridges to provide their customers with complex genetics analysis at a large scale and at low cost—typically saving researchers 40% compared to in-house solutions.

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By using AWS, Arterys can render multi-dimensional models of the heart across all device types in 10 minutes or less instead of the 90-minute industry standard.

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Using AWS, the Smithsonian Institution Data Science Team can scale instances up and down as needed, allowing the team to annotate genomes in parallel while also managing costs.

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With AWS, QBiC can potentially cut their genomics research time by 50 percent because of the automation and orchestration they get with AWS Batch.
Fred Hutch aims to accelerate their research processes on AWS so they can get closer to developing therapeutics to fight cancer. 

Oil and Gas


Hess uses AWS to ingest Seismic data at massive scale and improve productivity of their engineers.

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With Spiral Suite running on AWS, a problem that once would have required about seven hours of calculation time completes in less than four minutes.

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With AWS, Woodside data science teams can deliver a new analytics algorithm in a few weeks, where it would have taken them three to six months previously.  

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Digital Globe was able to get data to their customers in the shortest amount of time using AWS

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By using cutting edge reservoir modeling software from Rock Flow Dynamics on AWS, simulations that would have taken several years to complete was done over a 12-day period.

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Financial Services

Aon Securities Inc. (ASI) leverages GPU-optimized instances on Amazon EC2 to run its financial simulation platform and lower the calculation and total reporting process time from 10 days to 10 minutes.
Bankinter uses AWS for credit-risk simulation applications that require upto 5,000,000 simulations. Using AWS, Bankinter was able to reduce the average time-to-solution from 23 hours to 20 minutes.
To respond to rapidly changing market dynamics, FINRA moved about 90 percent of its data volumes to Amazon Web Services, using AWS to capture, analyze, and store a daily influx of 37 billion record.
Using AWS, Pacific Life can quickly scale its compute capacity with less cost and IT overhead compared to adding new hardware to its own data centers.

Autonomous Vehicles

TuSimple built their autonomous driving platform using sophisticated deep learning algorithms developed with Apache MXNet on AWS. TuSimple has used AWS to simulate billions of miles of driving.
AWS provides DriveAI the ability to seamlessly scale as their business grows, and the capability to handle their high performance computing demands.

Universities & Academia (Research Computing)

Learn how UC Santa Cruz Genomics Institute was able to process samples faster and securely get results to collaborators using AWS.
Baylor College of Medicine was able to create a central and secure location for storing hundreds of terabytes of genomic data  and complete its own studies 5x faster than with their local infrastructure.
The Guttman Lab at the California Institute of Technology uses an AWS-based HPC cluster to quickly add new compute nodes, analyze genomic sequencing data in days instead of weeks.
By moving its research portal to AWS, Penn State made it easy for 6,000 researchers worldwide to design more than 50,000 synthetic DNA sequences, using Penn State’s design methods and optimization algorithms.

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The Algorithms, Machine, and People (AMP) Lab at the University of California Berkeley    leverages Amazon Web Services (AWS) to quickly scale the compute resources needed to analyze the algorithms that are used in genomics work.

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The HPC facility of the New York University (NYU) Center for Health Informatics and Bioinformatics uses AWS to allow medical informatics and bioinformatics researchers to share data and enable research computing needs that exceed local capacity limits.

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San Francisco State University researchers were able to complete research that previously would have taken them weeks in just hours while reducing computing costs by using Amazon EC2 and machine learning techniques

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