What is High-Performance Computing?
High-performance computing (HPC) is an IT infrastructure strategy that combines groups of computing systems to perform simultaneous, complex calculations across trillions of data points. A single computing system is limited in its processing capacity by its hardware and is less useful in running simulations for fields like climate modeling, drug discovery, genomic research, and more. HPC technologies can use multiple computing systems in parallel to increase processing speed exponentially.
In recent years, HPC technologies have evolved from running scientific simulations to running AI models and workloads at scale.
What are HPC use cases?
Several use cases of high-performance computing exist across academia, industry, and business.
Media and entertainment
High-performance clusters provide the compute power needed to render videos and 3D graphics, stream live events with high video quality, and process CGI. HPC clusters allow media businesses to reduce production timelines, expedite video encoding, and cut costs in the production process.
Healthcare and genomics
The healthcare industry uses HPC in numerous ways, from genome sequencing to protein structure prediction, and even in drug discovery initiatives. AI-powered models running on HPC clusters further help improve drug research and adoption.
In hospitals, high-performance computing works alongside AI software to help identify disease on image scans, create personalized medical treatment plans, and optimize medical record management.
Government and defense
High-performance computing is a supportive technology that provides resources to several defense solutions, like cryptography, real-time surveillance, intelligence analysis, and threat detection. Accessing a scalable resource system helps ensure governments have the computing power they need to carry out national security initiatives, military simulations, and more.
Climate modeling
Simulating the flow of fluid systems across the Earth, for weather reports and to generate climate data, requires processing enormous amounts of data concurrently. HPS offers the computing power needed to rapidly assimilate and process data, helping to provide insight to agencies that predict natural disasters, monitor weather systems, and forecast long-term climate change.
Financial services
Financial services, like hedge funds, insurance agencies, and banks, use HPC to process the data they need to run forecast models, predict credit risk, and optimize portfolios. The computing power that HPC offers improves data analytics with real-time insight.
Automotive sector
High-powered computing is a vital technology in computational fluid dynamics, materials testing, and crash simulation tests for the automotive industry. HPC offers rapid prototyping and real-time optimizations of designs, and it helps to simulate factory workflows. HPC is also a central technology in self-driving cars and real-time computer vision-based decision-making.
Cybersecurity
High-performance computing allows network administrators to analyze traffic to detect anomalies and identify potential threats before they occur. HPC also provides computing resources for encryption, system-wide assessments, and real-time threat neutralization.
How does HPC work?
High-performance computing aggregates the computing power of several individual servers, computers, or workstations to provide a more powerful solution. This process of many nodes working together is known as parallel computing. Each individual machine in this system is called a node, with many nodes coming together to form a cluster. Each node in the system is responsible for managing a different task, and all work in parallel to boost processing speed.
Cluster nodes
HPC solutions include a few node types
- Controller nodes coordinate work across the wider cluster system.
- Worker nodes, or compute nodes, carry out any processing.
- Interactive nodes, or login nodes, allow users to connect to the HPC system through the command line or a GUI.
HPC clusters can either be heterogeneous, when each node offers different hardware, or homogeneous, when each node has a similar performance capacity.
HPC cluster structures
There are two main HPC cluster structures.
Cluster computing
Cluster computing, also known as parallel computing, is where a collection of clusters work together on a similar function and in a similar location. This structure minimizes latency between nodes by having a similar network topology and being physically close.
Distributed computing
Distributed computing can use clusters that are either in a similar location or distributed across the globe. This cluster format can draw from on-premise hardware alongside cloud resources, providing a more flexible and scalable approach to HPC.
How do HPC jobs work?
HPC systems run two different kinds of processes, known as loosely coupled and tightly coupled workloads.
Loosely coupled workloads
Loosely coupled workloads are tasks that an HPC system completes independently from other functions that may occur in parallel within the system. Many independent tasks occur concurrently, so this form of HPC processing is sometimes called parallel workload jobs.
For example, when rendering a video, each frame acts as a different task. While each node that renders a frame may draw from the same storage, its ability to finish the task does not depend on any other node completing its task.
Tightly coupled workloads
Tightly coupled workloads are HPC processing tasks that depend on one another to complete the overall job. These workloads use a cluster’s shared memory and storage to share information amongst all the nodes in the cluster, helping each to concurrently complete its task. Tightly coupled workloads often require real-time coordination, with many nodes working to provide small pieces of information to complete a larger task. For example, each node may be responsible for simulating a distinct physics component in a weather forecast, and combining information from all nodes is necessary to render the final weather forecast.
What is HPC in the cloud?
High-performance computing in the cloud allows businesses to take advantage of HPC solutions without managing the HPC cluster they utilize. Instead of constructing an expensive on-premise data center, HPC in the cloud is a cost-effective solution that offers businesses the scalable compute power they need.
Three converging trends have accelerated the expansion of HPC cloud services.
Low-latency RDMA networking
The use of remote direct memory access (RDMA) has enabled networked nodes to access memory without requiring the use of their operating system. This approach ensures that one node can interact with another without interrupting its processes, removing process bottlenecks, minimizing latency, and maximizing throughput.
Increased demand for cloud computing
Due to the wide range of use cases for HPC, many businesses across various industries now need HPC services. HPC in the cloud allows these companies to access HPC services without constructing their own data centers, making this technology significantly more accessible.
Widespread AI use
Another cause of the increasing demand for HPC cloud services is the widespread use of AI and machine learning software. Generative AI tools need a great deal of computing power, with HPC providing these systems' computational resources and scalability. HPC is an effective solution for businesses that want to utilise enterprise-scale AI tools.
What are the benefits of HPC in the cloud?
There are several benefits of running HPC in the cloud.
Unified and remote management
Every HPC project has unique infrastructure requirements. Self-purchasing restricts the organization to a few select configurations that it can invest in. However, HPC cloud allows organizations to choose and combine diverse configurations of storage, compute, networking, and login nodes, GPUs, and workstations as required for their project. They can use a management console to interact with all these systems from a central location. This streamlines workflows and automates cluster functions for added convenience.
Dynamic resource provisioning and scaling
High-performance cloud computing systems allow businesses to scale their compute resource usage dynamically, effortlessly scaling up or down to meet demand. This flexibility improves efficiency and optimizes resource usage.
Managed updates
HPC workloads that are managed through cloud computing providers automatically issue updates to keep your systems up to date. This approach ensures that your HPC solutions are always up-to-date and offering the most effective possible service.
Flexibility to use custom applications
Businesses can bring their applications to their cloud provider. They can customise the operating system and pre-installed software to meet specific workload requirements.
How can AWS support your HPC requirements?
AWS HPC fully managed services allow you to accelerate innovation with virtually unlimited HPC cloud infrastructure. For example
- AWS Parallel Computing Service offers a fully managed service that you can use to build complete, elastic environments that can host your high-performance computing workloads.
- AWS ParallelCluster is an all-in-one open-source cluster management tool that simplifies managing HPC clusters on AWS.
- Amazon Elastic Fabric Adapter helps users to run HPC and ML applications at the scale they need, offering the ability to scale to thousands of GPUs or CPUs.
- Amazon DCV is a remote display protocol that helps customers access a secure way to deliver remote desktops and application streaming over various network conditions.
Get started with high-performance computing on AWS by creating a free account today.