High performance computing (HPC) is the use of aggregated processing power to perform complex or high volume computations. HPC systems are composed of nodes (machines) that are clustered together. These clusters process distributed workloads using parallel processing. In this article, you will learn how HPC and Artificial Intelligence (AI) can work together to improve HPC technologies and AI implementations.
AI and HPC: How HPC Can Help You Build Better AI Applications
HPC systems typically have between 16 and 64 nodes, each with two or more central processing units (CPUs). This provides significantly greater processing power than traditional systems that have only one or two CPUs in a single device. Additionally, nodes in HPC systems each contribute memory and storage resources allowing for much greater storage and speed than traditional systems.
To further boost processing power, many HPC systems incorporate graphics processing units (GPUs). GPUs are purpose-built processors that you can use in tandem with CPUs as co-processors. When CPUs and GPUs are combined, it’s called hybrid computing.
There are several ways HPC systems can contribute to the development of artificial intelligence, including:
Specialized processors—GPUs enable you to more efficiently process AI-related algorithms, such as those used by neural networks.
Speed—parallel processing and co-processing greatly speeds computational processes. This enables you to process data sets and run experiments in less time.
Volume—large amounts of storage and memory enable you to run longer analyses and process significant volumes of data. This helps you improve the accuracy of artificial intelligence models.
Efficiency—enables you to distribute workloads across available resources. This allows you to maximize resource use.
Cost—HPC systems can provide more cost-effective access to supercomputing. If you use cloud resources, you can access HPC with pay as you go pricing and avoid upfront costs.
The Convergence of HPC and AI
HPC and AI are well matched in a variety of ways. HPC can better support AI model training than traditional systems can. Meanwhile, you can use AI to more intelligently queue and process workloads, maximizing the resources of HPC systems.
In addition to the processes HPC and AI perform, both also share a need for a high-performance infrastructure. These infrastructure requirements include large volumes of storage and computing power, high-speed interconnects, and accelerators. This overlap is understood by users of both technologies and many are trying to leverage these similarities and mutual benefits to develop more powerful tools.
However, one caveat is that these technologies are managed in very different ways. Where HPC workloads are often managed with Slurm or PBS Pro, AI applications are frequently hosted in containers and managed with Kubernetes. Overcoming this difference and integrating tooling is sure to be the next big step for these technologies.
AI and HPC Together
There are many ways in which AI and HPC are starting to combine. A few of the most promising ways are covered below.
Until now, most HPC run programs were written in either Fortrans, C, or C++. Likewise, the accelerators that HPC uses are typically supported with C interfaces, libraries, and extensions. While other languages are possible to use, there isn’t the same support or available code for users to embrace.
However, AI is often based on other tools and languages. To integrate the two, users are beginning to develop HPC applications and interfaces that support AI tools, such as Python and MATLAB. This is a relatively easy process since these programs will still be based on C or C++ routines.
Containers and containerized workloads have taken the spotlight in recent years. Containers enable more agile deployments, can provide higher availability, and offer better resource optimization than traditional applications and services.
Many AI applications are already hosted in and operated from containers. Partially due to this, people are looking at ways to host HPC processes in containers as well. HPC has already started to move to the cloud, offered as a service by vendors like Azure, AWS, and GCP. Containerization is a logical next step.
Big data is everywhere, supported by seemingly endless devices, websites, and profiles. However, all of this data is only useful when there are tools equipped to process it. Both AI and HPC can help with this.
HPC systems can help you ingest, process, and transform data in real-time. This prepares data for analysis, which frequently incorporates AI. When the systems combine, data can be analyzed and modelled significantly faster. Additionally, since there is no need to transfer data between systems, it’s easier to secure and you’re able to save significant amounts of bandwidth.
How HPC is Accelerating AI in the Automotive, Biomedical and Healthcare Industries
There are many real-world applications for HPC and AI, from meteorological predictions to fraud detection. Below are three examples that can give you a better idea of the benefits this pairing can provide.
Most people are at least familiar with the idea of autonomous vehicles. These are vehicles that can self-correct lane placements, park without assistance, or automatically call authorities in case of an accident. Autonomous vehicles expand upon the existing AI capabilities that already help drivers navigate and stay safe.
To accomplish this, these vehicles must collect, process, and analyze significant amounts of sensor data every second. However, before that, the algorithms that these vehicles use to predict situations and determine actions must be trained.
This training can be done on HPC systems. After models are trained, in-vehicle HPC systems can be used to run these models and process data in real-time. Without HPC, AI models cannot be run fast enough to be effective or functional.
As technology advances, biomedical research is growing more and more digital. There are now vast streams of biological data that researchers can access and analyze. This data is generated by a variety of technologies, from genome sequencing to cryo-electron microscopes. To process this data, many researchers are running HPC supported AI-based analytics.
One common use example of this is drug research. This research uses HPC to construct highly detailed, biological models that can simulate biological processes down to the subcellular level. These researchers then apply AI to determine how possible drug structures and combinations might affect human functioning.
Modern hospitals constantly generate data, from patient records, to clinical trials, to medical decision making. Effectively analyzing and applying this data to improving patient care, clinician effectiveness, and hospital operations requires advanced tools. For example, HPC systems and AI models.
One of the ways in which HPC and AI are being used is to create more reliable diagnostic tools. For example, image recognition models are being trained to accurately identify whether masses are benign or cancerous tumors. To accomplish this, researchers train models against large amounts of generated data that has been created by HPC systems. This data is used to refine the diagnostic model in the hopes that it can someday be applied to pre-screening patients or catching tumors earlier in development.
HPC and AI are a perfect match. When these two technologies are employed for the purpose of aiding each other, AI can gain the speed it needs to become smarter, and HPC can gain smart capabilities needed to provide better results. Combined with big data, AI gets the data it needs to learn, and HPC helps the AI algorithm go through the data faster. These added capabilities can help improve AI applications in healthcare, biomedical, and automotive fields.
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Thank you to Gilad David Maayan for contributing this article. Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Samsung NEXT, NetApp and Imperva, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership.