Why Machine Learning Models Demand High Performance Computing (HPC)

Data centers supporting HPC and machine learning

Your developers created machine learning models to identify patterns and trends in content and data. This will enable your management team to tap into unseen intelligence to make decisions on how to run your company and serve customers more efficiently. You’re ready to leverage artificial intelligence to the hilt.

Of course, those models also need sufficient compute resources to crunch through your large data volumes and content repositories. Only then can the complex algorithms accelerate model training and inference. That’s where High-Performance Computing (HPC) comes into play.

In this blog, we examine why machine learning models demand HPC resources. We also explore how HPC resources depend heavily on colocation data centers. HPC demands high-density racks that deliver sufficient power and cooling to keep server clusters running optimally—so the machine learning models can continuously analyze data and improve their outputs.

The Complexity of Machine Learning Models

Machine learning models use complex processes, methodologies, and frameworks to govern how applications learn from data and predict outcomes. These include training algorithms to recognize patterns and process information—without explicit programming.

The models also rely on preprocessed data as an interplay among various learning techniques:

• Supervised (regression and classification)
• Unsupervised (clustering and dimensionality reduction)
• Reinforcement (rewards by trial and error)

Each of these adds layers of complexity. Developers then select an appropriate machine learning model. The algorithm learns from training the data and adjusts internal parameters based on the input features and the desired outputs. During this step, any adjustments must occur iteratively to train the data for that model.

HPC Provides the Firepower to Support Machine Learning

Machine learning models require the increased processing power of HPC to handle their multi-layered architectures, rigorous training requirements, and inference processes. HPC supercomputers with parallel processing techniques efficiently handle these tasks that would otherwise take an impractically long amount of time on standard computing systems.

One of the key drivers for the use of HPC to run machine learning models is the immense volume and variety of data that modern models process. Big data causes an explosion of information, with enterprises creating petabytes daily. Data also comes in various forms—ranging from structured databases and tables to unstructured text from social media and blogs and semi-structured files.

HPC takes on this challenge by employing powerful compute resources (such as Nvidia H100 processors) advanced software, and optimized architectures to enable significant processing speed and performance gains. When applied to machine learning, HPC can process vast quantities of data lightning-fast. This enables more accurate models that produce insights previously unattainable from the data.

The Required Infrastructure for HPC

For companies that use HPC to drive their machine learning models, deploying the right infrastructure proves just as vital. The leading colocation data center providers deliver ideal infrastructures for HPC environments.

Here are some of the key attributes:

• Availability: Reliable and redundant power and cooling systems as well as backup generators to make sure computing systems run at all times.
• High-Speed Connectivity: With multiple carrier networks and nearby network hubs, customers can choose interconnections that provide low-latency connectivity for machine learning models. This speeds up handling the large data volumes transferred among computing systems.
• Scalability: A range of rack and server configurations, customizable power and cooling options, and on-demand scaling allow HPC systems to quickly adapt. This allows machine learning models to meet changing business requirements—without costly and time-consuming infrastructure upgrades.

Another key benefit colocation data centers provide is reduced costs. Customers can outsource the management of their computing infrastructure to avoid the capital expense of building and maintaining a data center that supports HPC and machine learning.

Experience with Supercomputing Environments

If your enterprise needs help deploying an HPC environment to support applications with machine learning models, DataBank is ready to help. We deliver high-density colocation HPC solutions in supercomputing environments such as Georgia Tech and the University of Maryland. We also support organizations in the financial services, cloud, software, and AI sectors.

We can help you determine exactly what you need from your data center. Our colocation facilities also take advantage of our Universal Data Hall Design framework—built on flexibility to accommodate deployments of varying sizes, layouts, and technology.