The convergence of HPC, AI, and data analytics has driven many infrastructure teams to seek HPC systems that support both compute centric and data centric usages with a single pool of resources. Other infrastructure teams, however, may prefer HPC clusters focused either on traditional HPC workloads for simulation and modeling or on data analytic workloads for AI usages.
Many customers spend excessive time and resources considering the 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 to specify, build and tune clusters optimized for their needs.
