AI-Driven Workflows in High-Performance Computing Require Tailored Resource Allocation
AI-driven workflows in high-performance computing (HPC) necessitate distinct phases with varying computational profiles, demanding tailored resource allocations for training, inference, and simulation stages. Unlike traditional, deterministic HPC applications, modern AI-driven configurations are iterative, data-dependent, and probabilistic, introducing cycles of model training, batch inference, validation, and automated refinement. Treating these phases as equivalent can lead to inefficient resource requests, idle accelerators, and increased queue pressure. For instance, training jobs often require sustained GPU use, high memory, and long allocations, while inference tasks are typically lighter and more easily parallelized. Simulation or validation stages may require CPU nodes, MPI execution, or long-running batch jobs. Effective workflow design must explicitly integrate AI components, manage intermediate data, and containerize environments to ensure reproducibility and efficient resource utilization acros...