The utilization of computing resources on New York University’s High-Performance Computing (HPC) clusters involves submitting and running computational tasks to solve complex problems. This process encompasses various stages, including resource allocation requests, job scheduling, and execution of user-defined applications, often within a batch processing environment. For example, researchers might employ these systems to simulate molecular dynamics, analyze large datasets, or perform intensive numerical calculations.
The effective management and analysis of how these computing resources are used are crucial for optimizing cluster performance, informing resource allocation strategies, and ensuring equitable access for all users. Understanding patterns of resource consumption allows administrators to identify bottlenecks, predict future demands, and ultimately improve the overall research productivity enabled by the HPC infrastructure. Historical analysis reveals trends in application types, user behavior, and the evolving computational needs of the NYU research community.
