Artificial Intelligence (AI) has emerged as a world-changing technology with a wide range of applications across industries. From virtual assistants to autonomous vehicles and advanced data analytics, AI has started to revolutionize the way we live and work. However, most AI algorithms require a very large amount of computational power to process and analyze all the necessary data. This is where High-Performance Computing (HPC) can be of great use.
What is High-Performance Computing?
High-Performance Computing refers to the use of supercomputers or compute clusters that have massive computational power to solve large computational problems and process vast amounts of data. HPC systems are designed to deliver high processing speeds, large storage capacities, and efficient data transfer rates. These systems are capable of performing billions of calculations per second, making them ideal for handling the intense computational requirements of AI algorithms.
How does HPC work?
An HPC system is a group of computers (nodes) that are interconnected to form a cluster. Each node in the cluster has an operating system, a processor, storage and networking capabilities so the nodes can communicate with each other. This cluster is then connected to a user input and file storage to perform large computational tasks.
The computer cluster is able to do these tasks thanks to parallel processing. A large task that is given through a user input can be divided into smaller subtasks. Via a high-speed interconnect, these subtasks are each assigned to a node in the cluster. The node performs this subtask, and through the network connection between the compute nodes it combines its solution to the solutions of all the other subtasks. Because these subtasks can all be performed at the same time, the HPC system will finish computing much quicker than a regular computer can.
Additionally, HPC systems use high-end hardware to further speed up computations. The processors in the nodes are usually the latest GPUs and CPUs. GPUs are particularly well-suited to executing AI algorithms like training neural networks. The processing speed gained by making use of parallel processing can be used to process larger volumes of data, which translates to more accurate AI models.
You can take this a step further by involving the cloud. While you could build your own cluster, it is much more efficient – both in time and cost – to make use of the hardware that is already out there and ready for your computational jobs.
How is HPC relevant to AI?
AI algorithms, particularly those based on machine learning and deep learning, require substantial computational resources to train models and perform inference tasks. Training AI models involves processing massive datasets through multiple iterations, adjusting weights and parameters to improve accuracy and performance. HPC provides the computational power necessary to accelerate these training processes, reducing the time required to develop and refine AI models.
How HPC can help build better AI applications
So we’ve established that HPC and AI are complementary technologies: HPC provides the computational muscle needed to process and analyze large datasets, while AI algorithms offer the intelligence to extract meaningful insights from the data. Together, they enable organizations to tackle complex problems, make data-driven decisions, and develop advanced AI applications.
Why is HPC important for your business?
HPC can help businesses with their increasing need for real-time insights. By leveraging HPC, businesses can accelerate their AI initiatives, enhance decision-making processes, and therefore gain a competitive edge in their respective markets.
Who is using HPC?
The people who make use of HPC are those who have large amounts of data to process. To name a few, HPC can be useful to:
- Researchers
- Data Scientists
- Engineers
The global HPC market value is expected to reach almost $60 billion by 2030. This is because so many different fields of work now work with very large amounts of data. This ranges from aerospace engineering to genomics, and from financial technology to drug discovery.
For example, in aerospace engineering, HPC can be used to create complex simulations to gain insights into the aerodynamics of various aircraft designs.
Why should you utilize HPC in your business?
High-performance computing has many advantages that are of interest to business owners and managers. These advantages include:
- Speed: the speed at which HPC systems can deliver solutions to large computation problems is – no pun intended – unparalleled.
- Cost saving: this computational speed directly translates to lower cost. In addition, on platforms like UbiOps it is possible to only pay for the computational power you use, which means you can scale to zero if you want, and easily scale up if you need.
- Innovation: with HPC it is possible to perform analyses that are otherwise impossible or extremely time consuming.
The roles of CPUs and GPUs in HPC
Central Processing Units (CPUs) and Graphics Processing Units (GPUs) are key components in HPC systems. In short, CPUs are good at handling sequential tasks, while GPUs are processors designed for parallel processing. GPUs are particularly effective in accelerating AI workloads due to their ability to perform thousands of computations simultaneously. By combining the computational power of CPUs and GPUs, HPC systems can efficiently handle diverse workloads, from general-purpose computing to AI-specific tasks.
HPC and UbiOps
With UbiOps, it is possible to make use of state-of-the-art GPUs and CPUs to accelerate your AI, all in the cloud. When you deploy your machine learning model to UbiOps, we create a microservice with its own API endpoint. This means you can integrate our processors into your own data pipelines. You only pay for the computational power you use, so there are no unexpected costs and you can scale to zero whenever you want.
Are you interested in how that works? Book a demo with us!
