How To Leverage High Performance Computing (hpc) For Ai

By Author: Penguin Solutions
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Using a group's combined processing capacity to carry out intricate or many computations is known as High Performance Computing (HPC). Nodes (machines) that are clustered together make up HPC systems. These clusters use parallel computing to handle distributed workloads. Learn how HPC and Artificial Intelligence (AI) can cooperate in this article. We will also determine the causes behind the migration of HPC and AI workloads to the cloud.

HPC and AI: How It Aids in the Development of Better AI Applications

The normal number of nodes in an HPC system is between 16 and 64, and each node has two or more CPUs. Compared to conventional systems, which only have one or two CPUs in a single device, this offers noticeably more processing capability. Nodes in HPC systems also contribute memory and storage resources, which results in significantly more capacity and speed than traditional systems.

Field-programmable gate arrays (FPGAs) and graphics processing units (GPUs) are commonly used in HPC systems to increase computing power. These are processors that have been specifically designed to run deep learning techniques, ...
... which are at the core of most AI models, a great deal quicker than conventional CPUs.

HPC systems can advance the development of artificial intelligence in a number of ways, including:

Specialized processors

You can process AI-related algorithms, including those employed by neural network models, more effectively with GPUs.

Speed

Co-processing and parallel processing greatly accelerate computer operations. You can handle data sets and conduct experiments more quickly as a result.

Amount

You can run longer analysis and analyze huge amounts of data with a lot of storage and memory. By doing this, you can increase the precision of artificial intelligence models.

Performance

You can disperse workloads across available computing technologies and resources by using high speed computing and AI. You may maximize resource usage in this way.

Cost

Access to high speed computing with big data and AI-powered can be more affordably provided by HPC systems. You can use HPC with pay-as-you-go pricing and avoid upfront expenditures if you employ cloud resources.

The Merging of AI and HPC

Advances in HPC go hand in hand with the development of AI. Compared to conventional systems, HPC can assist AI model training more effectively. While this is going on, AI can be used to more effectively queue and process workloads, making the most of HPC systems' resources.

AI and HPC both require high-performance infrastructure in addition to the tasks they carry out. These infrastructure requirements include accelerators, high-speed interconnects, enormous amounts of storage and computational power. Users of both technologies are aware of this overlap, and many are attempting to take use of these parallels and synergies to create stronger tools.

The fact that these technologies are controlled in such disparate ways should be noted. AI applications are commonly hosted in containers and managed with Kubernetes, in contrast to HPC workloads, which are frequently managed with Slurm or PBS Pro. The next significant development for these technologies will undoubtedly be overcoming this disparity and combining tooling.
AI and HPC Together

HPC and AI can be coupled in a variety of ways. The following lists a few of the most promising ones.

Advanced Programming

Up until this point, the majority of HPC run programmes were created in Fortran, C, or C++. Similar to this, C interfaces, libraries, and extensions are often used to support the accelerators that HPC uses. Although it is feasible to use other languages, there isn't the same support or available code for users to adopt.

AI, however, frequently draws from other resources and dialects. Users are starting to create HPC programmes and interfaces that enable AI tools, such as Python and MATLAB, in order to merge the two. Since these programmes will still be based on C or C++ functions, the approach is rather simple.

Containers

In recent years, containerized workloads and containers have dominated the media. Compared to traditional apps and services, containers enable more rapid deployments, offer higher availability, and offer better resource optimisation.

Numerous AI applications are already hosted in containers and run from them. People are looking into ways to host HPC processes in containers in part because of this. HPC is already beginning to migrate to the cloud, where it is being provided as a service by companies like Azure, AWS, and GCP. The logical progression is containerization.

Huge 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. Additionally, it is simpler to secure and you can save a lot of bandwidth because there is no need to transport data between systems.

Conclusion

Development services for AI and HPC go together perfectly. When these two technologies are used to complement one another, AI can advance at the rate necessary to grow smarter, and HPC can advance to the level of intelligence required to deliver superior outcomes. AI receives the data it requires to learn using a specific deep learning model when combined with large data. The AI programme processes the data more quickly thanks to HPC. The healthcare, biomedical, and automotive industries can all benefit from these new capabilities.

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