High Performance Computing (HPC) allows scientists and engineers to solve complex, compute-intensive problems. HPC applications often require high network performance, fast storage, large amounts of memory, very high compute capabilities, or all of these. AWS enables you to increase the speed of research and reduce time-to-results by running HPC in the cloud and scaling to larger numbers of parallel tasks than would be practical in most on-premises environments. AWS helps to reduce costs by providing CPU, GPU, and FPGA servers on-demand, optimized for specific applications, and without the need for large capital investments.
Instantly launch or scale up High Performance Computing clusters on AWS. By eliminating job queue times and scaling your cluster as high as needed, when needed, you can reduce the time to market or publication.
Focus on applications and research output over infrastructure maintenance and upgrades. When AWS upgrades hardware, you can gain access instantaneously — simply rewrite your cluster configuration file and reboot to move to the latest hardware.
Let your research dictate infrastructure, not the other way around. With the flexible configuration options AWS provides, you can start with your hypothesis and create HPC clusters that are optimized for your unique application requirements – GPU today, CPU tomorrow.
In addition to core service options for compute, storage, and databases, take advantage of the breadth of services and partners in the AWS ecosystem to enhance your workload. Options range from familiar solutions like NICE and Thinkbox to experimental builds with AWS Lambda.
Collaborate without compromising on security. Every AWS service provides encryption and options to grant granular permissions for each user while maintaining the ability to share data across approved users. Build solutions compliant with HIPAA, FISMA, FedRAMP, PCI, and more.
Let every dollar contribute meaningfully to your mission. Choose from a range of AWS services and only pay for what you use. No more paying for idle compute capacity, no long-term contracts, and no complex licensing involved. Optimize costs further with Amazon EC2 Spot Instances.
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Life Sciences
Genomics
The Algorithms, Machine, and People (AMP) Lab at UC Berkeley leveraged AWS to quickly scale the compute resources needed to analyze the algorithms that are used in genomics work. Learn more >>
Computational Chemistry
Novartis built a platform leveraging AWS to run approximately 87,000 compute cores to conduct 39 years of computational chemistry in 9 hours for a cost of $4,232. Learn more >>
Biological Systems Simulation
Penn State moved its research portal to AWS and made it easy for 6,000 researchers worldwide to design more than 50,000 synthetic DNA sequences. Learn more >>
Protein Modeling
The Computer Science department at San Francisco State University used Amazon EC2 to reduce costs and turnaround time to run machine learning workloads. Learn more >>
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Financial Services
Capital Management and Reporting
MAPFRE saved 88 percent on infrastructure costs and gained the ability to spin up a supercomputer on demand and shut it down when finished. Learn more >>
Risk Management Portfolio Optimization
Yuanta Securities Korea benefits from increased speeds and lower costs by running financial models on AWS to assess market risk. Learn more >>
Contract Pricing and Valuation
Aon Benfield moved its infrastructure to AWS and built a processing system that reduced policy recalculation from hours or days to minutes. Learn more >>
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Design and Engineering
Electronics Design Automation
Cadence Design Systems used AWS to isolate workloads from one another and ensure users and applications didn’t compete for resources, which resulted in reduced regression times, quicker iterations, and shifted focus on optimization and agility. Learn more >>
Computational Fluid Dynamics (CFD)
TLG Aerospace used EC2 Spot Instances to access more memory and cores at a lower cost, allowing them to scale the number and size of increasingly demanding simulations. Learn more >>
Engineering Simulation
Ansys ran a simulations with Enhanced Networking-compatible EC2 instances and demonstrated near-ideal scalability well past 1000 cores and a reduced overall solution time even beyond 2000 cores. Learn more >>
3D Rendering
ZeroLight used AWS GPU instances to build an award-winning 3D car configuration tool to create a totaly new, interctive user experience for customers on the show room floor to select and configure thier next automobile.
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Energy & Earth Sciences
Weather Simulation
The Weather Company redesigned its big data platform, forecasting systems, and applications to run natively in a cloud environment and reduced their on-premises environments from 13 to six data centers, freeing engineers to build network and application efficiency. Learn more >>
Reservoir Simulation
Rock Flow Dynamics used on-demand computing resources to run workloads to optimize the location of oil wells and water injection wells. What would have taken several years to complete was done over a 12 day period using AWS resources. Learn more >>
Geographic Information Systems (GIS)
Digital Globe used AWS to deliver petabytes of high-resolution Earth imagery, data, and analysis to its customers in weeks instead of months while saving on costs. Learn more >>
Operations, Management, and Analytics
Fugro Roames used AWS and Amazon EC2 Spot Instances to enable Ergon Energy to reduce the annual cost of vegetation management from AU$100 million to AU$60 million. Learn more >>
High Performance Computing workloads on AWS are run on virtual servers, known as instances, enabled by Amazon Elastic Compute Cloud (Amazon EC2). Amazon EC2 provides secure, resizable compute capacity in the cloud and is offered in a wide range of instance types so you can choose one optimized for your workload.
High Performance Computing workload management gains new levels of flexibility in the cloud, making resource and job orchestration an important consideration for your workload. AWS provides a range of solutions for workload orchestration: fully-managed services enable you to focus more on job requests and output over provisioning, configuring and optimizing the cluster and job scheduler, while self-managed solutions enable you to configure and maintain cloud-native clusters yourself, leveraging traditional job schedulers to use on AWS or in hybrid scenarios.
AWS provides several options for storage, ranging from file systems attached to an EC2 instance to high performance object storage. Most HPC applications require shared access to data from multiple EC2 instances via a file system interface. AWS provides a native, scale-out shared file storage service (Amazon EFS) that provides a file system interface and file system semantics. HPC applications can also use AWS’ block storage offerings, either Amazon EBS or EC2 instance store, for general purpose working storage. Amazon S3 and Glacier provides low-cost storage options for long-term storage of large data sets.
The AWS network is designed for scale. Whether your application requires thousands of cores for one tightly-coupled workload, hundreds-of-thousands of cores for embarrassingly-parallel, high-throughput computing (HTC) applications, or a mixture of both, the AWS network offers performance (high bandwidth, low latency) and scalability.
AWS optimizes and custom builds their own hardware specifically for AWS infrastructure. Cut-through routing combined with AWS large scale means even the biggest customers see consistent latency and high bandwidth when using the most challenging application communication patterns. Enhanced networking provides higher I/O performance and lower CPU utilization compared to traditional virtualized network interfaces. This feature provides higher packet per second (PPS) performance, lower inter-instance latencies, and very low network jitter. Enhanced Networking is available in one of two ways and depending on the instance type: Intel 82599 or Amazon ENA.
From preparing simulation input data to interpreting computing job outputs, high performance graphics tasks are part of many HPC workloads. AWS offers several products to improve the performance, cost and flexibility of running OpenGL, Direct/X and other graphics applications. You can accelerate graphics performance by using the GPU-powered G2 and G3 instances or Elastic GPU, and stream Windows graphics with AppStream 2.0, WorkSpaces, or NICE DCV. If you prefer a Linux-based graphics platform, combining the streaming performance of NICE DCV and the EnginFrame HPC portal can deliver end-to-end workflows to end users across on-premises, hybrid cloud, or full-AWS configurations.
AWS offers you a pay-as-you-go approach for pricing for over 70 cloud services. With AWS you pay only for the individual services you need, for as long as you use them, and without requiring long-term contracts or complex licensing. AWS pricing is similar to how you pay for utilities like water or electricity. You only pay for the services you consume, and once you stop using them, there are no additional costs or termination fees. Learn more about how pricing works on AWS >>
There are three main ways to pay for your compute capacity on Amazon EC2: On-Demand, Reserved Instances, and Spot Instances.
| Compute Pricing Model |
Description |
Recommended HPC Use: |
| On Demand Instances | With On-Demand instances, you pay for compute capacity by the hour with no long-term commitments or upfront payments. You can increase or decrease your compute capacity depending on the demands of your application and only pay the specified hourly rate for the instances you use. |
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| Spot Instances | Spot Instances is a pricing model that enables you to bid on unused Amazon EC2 capacity at whatever price you choose. When your bid exceeds the Spot price, you gain access to the available Spot Instances and run as long as the bid exceeds the Spot Price. Historically, the Spot price has been 50% to 93% lower than the on-demand price. Learn more about optimizing scientific computing costs with Spot Instances >> |
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| Reserved Instances | Reserved Instances provide you with a significant discount (up to 75%) compared to On-Demand instance pricing. In addition, when Reserved Instances are assigned to a specific Availability Zone, they provide a capacity reservation, giving you additional confidence in your ability to launch instances when you need them. |
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AWS Partners provide professional services or software solutions to enable workloads on AWS. Browse our selection of featured partners and learn more.
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