Baker Hughes Reduces Time to Results, Carbon Footprint, and Cost Using AWS HPC

By Amazon Web Services

November 7, 2022

Baker Hughes migrated its computational fluid dynamics applications to AWS, cutting gas turbine design cycle time, saving 40 percent on HPC costs, and reducing its carbon footprint by 99 percent.

Overview | Opportunity | Solution | Outcome | AWS Services Used

40% reduction in HPC costs

98% reduction in wait time

26% faster runtime in resource-intensive HPC job

99% reduction in carbon footprint 

Overview

Engineers at Baker Hughes were using an on-premises high performance computing (HPC) solution to simulate gas turbine designs, but it couldn’t scale due to resource capacity bottlenecks. Engineers faced long simulation wait and run times with an increased need for physical prototypes. Baker Hughes chose to migrate its computational fluid dynamics (CFD) applications from on premises to Amazon Web Services (AWS). As a result, the company saved 40 percent on HPC costs, and reduced wait time by 98 percent, run time by 26 percent, and carbon footprint of the HPC solution by 99 percent, helping the company to achieve a faster time to results.

Image provided by AWS

Opportunity | Seeking an Elastic HPC Solution

For more than 100 years, Baker Hughes has been a global leader in industrial turbomachinery and innovation through its Turbomachinery and Process Solutions (TPS) Research Center. Based in Florence, Italy, TPS provides the turbine, compressor, and pump technology that is currently used by the energy industry. Its NovaLT gas turbines set new standards in greenhouse gas emissions, efficiency, and reliability.

To run simulations for designing gas turbines, TPS engineers had been using on-premises HPC solutions for CFD applications from Ansys, an AWS Partner. These included Ansys Fluent for fluid simulation, Ansys CFX for turbomachinery applications, and Ansys Mechanical for structural engineering. Resource capacity bottlenecks allowed limited simulations with long wait and run times for the engineers prior to running expensive and burdensome physical tests. “To remove this bottleneck and better manage the peaks, we needed to expand capacity to 400 teraflops, but we didn’t want to pay for peak capacity yearlong,” says David Meyer, director of digital operations for HPC and remote visualization for Baker Hughes. “We needed an elastic solution for an optimal total cost of ownership.”

Using the runtime performance of an Ansys Fluent job as a proof of concept, Baker Hughes compared cloud providers in early 2021. AWS Professional Services, a global team of experts that can help organizations realize desired business outcomes when using AWS, delivered the proof of concept within weeks and on budget, proving the best runtime performance. To accelerate its cloud migration and modernization journey, Baker Hughes used AWS Migration Acceleration Program (AWS MAP), a comprehensive and proven cloud migration program based upon the experience of AWS in migrating thousands of enterprise customers to the cloud. Baker Hughes used AWS MAP to optimize its cloud spend alongside the company’s use of Savings Plans and AWS Enterprise Discount Program, flexible and custom-tailored pricing models for AWS services.

“Running Ansys simulations on AWS helps TPS to accelerate its engineering schedules and achieve a faster time to market.” – David Meyer Director of Digital Operations for HPC and Remote Visualization, Baker Hughes

Solution | Simplifying Customer Experience and Improving Efficiency of HPC Jobs Using Amazon EC2

The solution went live in the fourth quarter of 2021. Now more than 150 TPS engineers in Italy, India, and the United States run as many simulations as needed prior to physical tests, leading to better accuracy with fewer test iterations. Plus, Baker Hughes onboards multiple users every month without impacting HPC job performance. “We were initially planning to migrate the equivalent compute capacity of 100 teraflops to AWS, but by giving engineers the possibility to scale, the consumption spiked by four times within 3 months of go-live,” says Yogesh Kulkarni, senior director, CTO India at Baker Hughes.

To run CFD simulations, Baker Hughes uses Amazon Elastic Compute Cloud (Amazon EC2), which provides secure and resizable compute capacity for virtually any workload. The solution accelerates HPC by attaching Intel-based Amazon EC2 instances to Elastic Fabric Adapter (EFA), a network interface for Amazon EC2 instances to run applications requiring high levels of internode communications at scale. EFA offers dedicated throughput of 100 gigabits per second per HPC job compared to the traditional network interface which offers 300 gigabits per second throughput shared across multiple HPC jobs. As a result, HPC jobs using EFA have low latency compared to the traditional network interface at a fraction of a cost. To further improve performance and reduce network latency, Baker Hughes deploys Amazon EC2 fleets of instances in placement groups, one per HPC job based on Shared-Nothing Architecture principle. Amazon EC2 spreads new instances across the underlying hardware as they launch, and placement groups influence the placement of interdependent instances to meet the throughput needs of the workload. By running on AWS, Baker Hughes avoids the issue of hardware lock-in that is inherent to an on-premises HPC solution. “For Ansys jobs, we now have the ability to use the best price-performance compute instances and continually onboard the latest generation processors as soon as they are available,” says Yogesh.

Baker Hughes uses several storage options on AWS for its CFD workloads….

Read the full case study to learn more. Reminder: You can learn a lot from AWS HPC engineers by subscribing to the HPC Tech Short YouTube channel, and following the AWS HPC Blog channel.

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