Symmetric multiprocessor (SMP) clusters have become the prevalent computing platforms for large-scale scientific computation in recent years mainly due to their good scalability. In fact, many parallel machines being used at supercomputing centers and national laboratories are of this type. It is critical and often very difficult on such large-scale parallel computers to efficiently manage a stream of jobs, whose requirement for resources and computing time greatly varies. Understanding the characteristics of workload imposed on a target environment plays a crucial role in managing system resources and developing an efficient resource management scheme. A parallel workload is analyzed typically by studying the traces from actual production parallel machines. The study of the workload traces not only provides the system designers with insight on how to design good processor allocation and job scheduling policies for efficient resource management, but also helps system administrators monitor and fine-tune the resource management strategies and algorithms.