The convergence of high-performance computing (HPC), AI, and data analytics has led infrastructure teams to seek HPC systems that support both compute-centric and data-centric uses with a single resource pool. However, some infrastructure teams may prefer HPC clusters that are focused on either traditional HPC workloads for simulation and modeling, or data-centric workloads for AI and data analytics.
Many customers invest significant time and resources in evaluating design choices, including network architecture, storage architecture, file systems, server configurations, CPUs, accelerators, memory, hard drives, operating systems, runtime and user-level libraries, workload managers, applications, and benchmarking. These efforts aim to specify, build, and tune clusters optimized for their needs.