Building High Performance Computing Clusters (GPU) for AI at Scale

Minyang Chen
13 min readJul 25, 2024

The use of GPU accelerated machine learning workloads. Multi GPUs and Multi-Nodes enable distributed training at scale. Supercomputers rely on multiple CPUs grouped into compute nodes.

A GPU cluster is a collection of computers that each have a graphics processing unit (GPU) enabled. Supercomputer clusters are collections of computing resources that can provide high-performance computing for users. It offers accelerated computation power to carry out tasks such as image and video processing, training neural networks, and other machine learning algorithms — train bigger and better models.

figure-1: high performance computing cluster (HPC)

Kubernetes is infrastructure clustering software that provides flexibility in managing and scheduling containerized workloads and microservices, offering high scalability.

Slurm is the workload manager on about 60% of the TOP500 supercomputers. Slurm manages and schedules Linux clusters, making use of its open-source platform’s fault tolerance and scalability. It is a suitable solution for clusters of various sizes.

Let’s take a look at the latest developments in AI infrastructure.

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Minyang Chen

Enthusiastic in AI, Cloud, Big Data and Software Engineering. Sharing insights from my own experiences.