What is High Performance Computing (HPC)
In general, high performance computing refers to the practice of aggregating computing power in order to solve large problems in science, engineering, or business at much higher performance than a typical desktop computer or workstation.
The supercomputer is one of the most well-known types of HPC solutions. A supercomputer is made up of thousands of compute nodes that collaborate to complete one or more tasks. This is known as parallel processing. It’s analogous to having thousands of PCs networked together, combining compute power to complete tasks more quickly.
Meaning of HPC Cluster
A collection of servers, known as compute nodes, are frequently used to form HPC clusters. When working together to address these complex computational workloads, clusters can range in size from a few nodes to hundreds or even thousands of compute nodes connected via a network.
The workloads are typically controlled by a batch scheduler and executed in batches. This scheduler is an essential part of HPC clusters; it keeps track of the resources that are available in a cluster, queues workloads when there are insufficient computational resources, and efficiently distributes tasks to resources as they become available.
HPC clusters and solutions are widely accessible and can be set up locally, in the cloud, or even as a hybrid solution that combines both on- and off-premise deployments.
How does HPC work?
Traditionally, computers work on a transaction-by-transaction basis, which means the next transaction, or job, doesn’t start until the previous one has been finished by the computer. In contrast, HPC executes numerous tasks simultaneously using all of the available resources or processors. As a result, the time needed to finish a task is determined by the resources available and the design chosen.
Additionally, the HPC system creates a queue if there are more jobs than there are processors.
There are some workloads that no single computer can handle, such DNA sequencing. With the help of individual nodes cooperating in a cluster to complete enormous quantities of computation in a brief amount of time, HPC or supercomputing environments handle these significant and complicated challenges.
Computing and data-intensive servers with strong CPUs that may be vertically expanded and made available to a user base are included in HPC systems. For workloads requiring a lot of graphics processing, HPC systems can also have a lot of powerful GPUs.
By using clusters, HPC systems can also scale horizontally. Networked machines with scheduler, computation, and storage capabilities make up these clusters. For instance, a single HPC cluster can have up to 100,000 processing cores. Clusters, as opposed to single server systems, can support numerous applications and resources for a user group. A cluster’s combined computational power and commodity resources may manage a dynamic workload while being scheduled according to rules.
Components of HPC
A network of numerous compute machines makes up an HPC cluster. A cluster could have tens of thousands of computers, depending on the demands of the application. A node is the name given to each server in an HPC cluster. These nodes operate in parallel to quicken processing and enhance system efficiency.
In order to design an HPC cluster, a person or business must select the appropriate hardware, allot power, space, and cooling, install the head nodes, monitor and manage them, and execute benchmarks and applications. Understanding HPC cluster components is essential for streamlining and enhancing the system’s performance.
The following are the essential components of HPC, at minimum.
1. Compute nodes
An HPC cluster’s processing element is called a compute note. They use local resources, such as CPU, GPU, FPGA, and other processing devices, to carry out the workload. Other resources on the compute node, including as the memory, storage, and network adapter, are also used by these workloads for processing.
Depending on how the workload employs various elements, its execution may be constrained by one or more of those. For instance, some memory-intensive tasks could have bandwidth or capacity restrictions. If data is written down to storage as part of the computation of a workload, workloads that either require a lot of data or generate a lot of data during computation may be constrained in their processing speed due to network bandwidth or storage performance limits. Some workloads could just require a lot of computational resources, and their capacity to be processed by the cluster may be constrained.
Inter-process communication is crucial for parallel HPC tasks, as was previously mentioned. When the communication occurs inside a compute node, it simply gets transmitted from one process to another through that node’s memory. However, a process must use the network to interact with a process running on a different computing node. This communication between processes could happen rather frequently. If so, it’s critical that the network has low latency to avoid delays in process communication. You don’t want to waste valuable compute time on processes that are waiting for message deliveries, after all.
The majority of HPC storage solutions are file-based. These file-based solutions can be divided into two types: general purpose and parallel storage solutions.
A shared file system called a parallel file system allows several clients to mount and access storage resources from various servers simultaneously. This provides clients with immediate access to data that has been saved, which reduces overheads by reducing abstraction and leads to low latency and fast performance.
In an HPC cluster, general-purpose storage has two primary functions. One would be to store the libraries and available application binaries. This is due to the necessity of having consistent binaries and libraries throughout the cluster when running an application, which makes central storage practical. The user should have consistent access to their data across the HPC cluster, therefore the other would be for the user’s home directories and other user data.
Benefits of HPC
For many years, HPC has been essential to the worlds of research and innovation. Professionals in the fields of engineering, design, science, and other disciplines have used HPC to achieve key discoveries. The key advantages of HPCs are listed below.
1. High Speed
HPCs are made for processing at high speeds. This means that HPC systems can complete large calculations that would have taken weeks or months with conventional processors in a matter of seconds.
2. Reduced Physical Testing
Most current applications must undergo testing physically before being released on the market. Simulations can be produced by HPC systems, which eliminates the need for physical experiments, which can be expensive and error-prone.
With more processing power, issues are swiftly resolved, resulting in less time and money wasted. Employers of HPCs can scale their spending as necessary and just pay for what they utilize.
Not every cloud provider is made equally. Some clouds aren’t built for HPC and can’t deliver the best performance when heavy workloads are running at their peak. Therefore, it is crucial to carefully select a cloud provider who is able to offer hpc services. Below are some considerations when selecting a cloud provider.
The cloud provider you choose should have extensive experience running HPC workloads for a wide range of clients. Furthermore, their cloud service should be designed to perform optimally even during peak periods, such as when running multiple simulations or models. In many cases, bare metal computer instances outperform virtual machines in terms of consistency and power.
Your cloud provider should use and maintain cutting-edge processor, storage, and network technologies. Make certain that they provide extensive capacity and top-tier performance that meets or exceeds typical on-premise deployments.
Make sure you know exactly what you’ll be paying for each time you use the service because cloud services are sometimes provided on a pay-as-you-go basis. The price of outbound data transportation, or egress, often surprises consumers. Although egress fees are readily disregarded, they are often associated with transactions and requests for data access.
Where is HPC used?
HPC solutions are utilized for a range of tasks across numerous industries and can be deployed on-premises, at the edge, or in the cloud. Examples comprise:
- Healthcare: Generating vaccinations, conducting medication research, and coming up with creative ways to treat both common and unusual disorders.
- Genomics: Analyzing medication interactions, sequencing DNA, and conducting protein analyses to support ancestry studies.
- Research labs: Scientists utilize HPC to develop novel materials, locate renewable energy sources, comprehend how the universe has evolved, and forecast and track storms.
- Media and entertainment: Editing blockbuster films, rendering mind-blowing special effects, and streaming live events around the globe are all done with HPC.
- Artificial Intelligence: HPC is employed to prevent credit card fraud, offer self-directed technical assistance, train autonomous vehicles, and advance cancer detection methods.
- Manufacturing: The use of simulations, such as those for autonomous driving, to assist in the design, production, and testing of new goods will lead to safer cars, lighter components, more efficient manufacturing techniques, and innovations.