A High Performance Computing (HPC) cluster is a collection of multiple servers, known as nodes, which enable the execution of applications. There are usually several various types of nodes in an HPC cluster. HPC software typically runs across hundreds or thousands of nodes to read and write data. Seamless communication is essential to enable communication between the storage system and large numbers of nodes.
HPC is rapidly evolving. We’ll discuss how HPC clusters are changing in response to cloud computing, the growing use of AI applications and models, the availability of powerful data center Graphical Processing Units (GPUs), and the overwhelming adoption of container infrastructure.
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An HPC cluster architecture typically includes the following components:
HPC is usually enabled by a purpose-built cluster for batch processing scientific workloads. You can combine on-premise clusters with cloud computing to handle usage spikes. The cloud is also suited to parallel jobs.
You can implement HPC using a public or private cloud. The major cloud providers allow you to build your own HPC services or leverage bundled services. Certain providers support ready-to-use HPC implementations in a hybrid environment.
Running HPC in the cloud offers the following benefits:
Cloud-based HPC generally requires the following components:
Typically, an HPC system contains between 16 and 64 nodes, with at least two CPUs per node. The multiple CPUs ensure increased processing power compared to traditional, single-device systems with only one CPU. The nodes in an HPC system also provide additional storage and memory resources, increasing both speed and storage capacity.
Some HPC systems use graphics processing units (GPUs) to boost processing power even further. You can use GPUs as co-processors, in parallel with CPUs, in a model known as hybrid computing.
HPC can provide the following benefits for developing artificial intelligence (AI) applications:
The following best practices can help you make the most of your HPC implementation.
Workflow optimization should be a priority when planning your cluster. HPC workloads can differ, so you may want to configure your cluster to optimize the operations of a specific workload.
HPC applications often divide a workload into a set of tasks, which can run in parallel. For some tasks, processing may require more communication between nodes and specialized resources (i.e., hardware/software).
The computational requirements of a workload determine how many compute nodes you need in your cluster, as well as the hardware required for each node and any software you should install. You will also need resources for maintaining security and implementing disaster recovery. To keep the system running, you will need to set up management nodes.
HPC workloads are usually monolithic, with HPC applications running large data sets in the data center. Containerized applications offer greater portability, allowing you to package HPC applications and run them across multiple clusters to handle large data sets.
Containerization enables a microservices architecture that can benefit application services. Building applications in a microservices environment typically involves packaging small services into containers. You can manage the lifecycle of each service independently, based on the specific granular scaling, patching, and development requirements of the service.
HPC application workloads can also benefit from the isolated management of development and scaling. The scalability of containers enables HPC workloads to handle spikes in data processing requirements without downtime. Containerized HPC applications can scale easily to accommodate these spikes.
Additional advantages of containers used in a microservices architecture include modularity and speed. You can use containers with multiple software components, dependencies, and libraries to run complex HPC applications. Containers help break down the complexity of HPC workloads and facilitate deployment.
Your GPU implementation has a significant impact on your HPC workloads. GPU providers typically offer virtualization software that can GPUs available to multiple computing resources.
HPC workloads typically require a single compute instance mapping to one or more GPUs. These configurations depend on technologies that allow VMs or bare metal machines to communicate directly with physical GPUs running on other machines.
Run:ai automates resource management and orchestration for HPC clusters utilizing GPU hardware. With Run:ai, you can automatically run as many compute intensive workloads as needed.
Here are some of the capabilities you gain when using Run:ai:
Run:ai simplifies HPC infrastructure, helping teams accelerate their productivity and conserve costs by running more jobs on fewer resources.
Learn more about the Run:ai GPU virtualization platform.
HPC on AWS: 6 Cloud Services and 8 Critical Best Practices
High performance computing differs from everyday computing in speed and processing power. An HPC system contains the same elements as a regular desktop computer, but enabling massive processing power. Today, most HPC workloads use massively distributed clusters of small machines rather than monolithic supercomputers.
Discover cloud services that can help you run HPC on AWS, and learn best practices for running HPC in the Amazon cloud more effectively.
HPC GPU: Taking HPC Clusters to the Next Level with GPUs
High-performance computing (HPC) is an umbrella term that refers to the aggregation of computing resources at large scale to deliver higher performance. This aggregation helps solve large, complex problems in many fields, including science, engineering, and business.
Learn how High-Performance Computing (HPC) clusters can leverage Graphical Processing Units (GPUs) to process AI/ML workloads more efficiently.
HPC and AI: Better Together
HPC refers to high-speed parallel processing of complex computations on multiple servers. A group of these servers is called a cluster, which consists of hundreds or thousands of computing servers connected through a network. In an HPC cluster, each computer performing computing operations is called a node.
Understand how High Performance Computing (HPC) is evolving to support Artificial Intelligence (AI) workloads, and learn about future convergence of HPC and AI.
Read more: HPC and AI: Better Together
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