How HPC and AI Work Together as a Powerful Combination

HPC and AI are both essential in their own right. What applications can HPC and AI overcome together?

Software that uses AI must have access to HPC clusters because of their processing power and scalability. On the other hand, AI enables us to utilize and process workloads on HPCbased infrastructure more intelligently.

So, what the enterprise and scientific computing landscapes can gain from the combined usage of HPC and AI is explained in this article. Determine whether combining these technologies would be a wise investment for your use case by getting started.

What is HPC?

High-performance computing (HPC) is the use of parallel computer clusters to complete large-scale tasks. HPC systems are ideal for activities involving massive data sets and intricate calculations because they can process and analyze data at incredibly fast rates.

HPC is crucial in use cases involving the following due to its extremely fast processing speed:

● Big data and enormous multidimensional data collection.

● data analytics in real-time.

● Extremely fast databases.

● AI and cutting-edge machine learning components

What is AI?

Artificial intelligence employs robots to carry out tasks as well as, if not better than, a human operator.

AI is more often associated with autonomous or self-directing systems in extremely specialized fields in modern applications. The machine can control other machines, oversee production procedures, or offer insights into media or data by using learned tactics.

How Does HPC Affect AI Development?

For many years, creating useful AI has been a challenging task. Due to limitations in hardware and software in previous years, AI catalyzed the development of several practical technologies in fields like robotics, language processing, and expert systems.

However, the development of HPC has made more immediately useful AI a reality. Let's examine a few scenarios and fields where combining HPC with AI makes sense.

1: Predictive Analysis

An excellent application case for the fusion of HPC and AI is predictive analytics.

● HPC offers the scalability and processing capacity required to manage big data collections.

● Real-time forecasts are made possible by AI approaches that improve accuracy.

Furthermore, HPC gear speeds up the training of AI models that make predictions. Data scientists can experiment with more complex algorithms more quickly thanks to faster AI training.

Predictive analytics combining HPC and AI is a welcome addition to many sectors (finance, healthcare, energy, supply chain management, cybersecurity, etc.).

2: Physics-Informed Neural Network

The incorporation of physics rules into inferencing models to produce more realistic results is one example of an AI-augmented HPC application case.

Neural networks used in these applications are referred to as physics-informed neural networks (PINNs) since they must abide by established rules including the conservation of mass, energy, and velocity.

For use cases such as fluid flow analysis, molecular dynamics, airfoil and jet engine design, and high-energy physics, PINNs can be used to supplement or even replace HPC modeling and simulation.

3: Containers

Recent years have seen a rise in the use of containers and containerized workloads. Contrary to conventional applications and services, containers allow for more agile deployments, improved availability, and better resource optimization.

Numerous AI applications are already running and being hosted in containers. People are considering ways to host HPC processes in containers as a result, in part. As a service provided by companies like Azure, AWS, and GCP, HPC has already begun to shift to the cloud. The next natural step is containerization.

4: Big Data

Big data is supported by an infinite number of gadgets, websites, and profiles. All of this information is useful, though, only if there are tools available to process it. HPC and AI both have applications in this.

You can ingest, process, and convert data in real time with the aid of HPC systems. This gets the data ready for analysis, which usually uses AI. The systems work together to considerably speed up data analysis and modeling. Also, it is simpler to secure and you can save a lot of bandwidth because there is no need to transport data between systems.

5: Climate Modeling

To accomplish some goals, researchers using climate modeling must replicate the Earth's climate:

● Recognize the climatic behavior.

● project future climatic trends.

● Evaluation of climate change's effects.

Running complicated climate models that must take into account a variety of physical, chemical, and geological elements is a task that HPC clusters are quite good at. On the other hand, AI is capable of analyzing enormous collections of climate data, spotting intriguing trends, and developing precise projections about the future of the environment.

Wrapping Up

New tools and approaches are accelerating the pace and scale of AI analysis enabling quick discovery and insights in HPC applications. More so than any legacy system, HPC can accommodate advanced AI models and algorithms.

Expect more business and scientific use cases to shift towards the combined use of HPC and AI in the upcoming years due to the two technologies' remarkable complementary nature.