Parallel application performance on shared, heterogeneous workstations
Gregory D. Peterson · 1994
The utilization of networked, shared, heterogeneous workstations as an inexpensive parallel computational platform is an appealing idea. However, most performance models for parallel computation are oriented towards the use of tightly-coupled, dedicated, homogeneous processors. We develop and validate an analytic performance modeling methodology for synchronous iterative algorithms executing on networked workstations. The model includes the effects of application load, background load, and processor heterogeneity. We use two applications, nonlinear optimization and discrete-event simulations to validate the model with homogeneous and heterogeneous workstation networks. While generating synthetic background load, we perform validation experiments under various loading conditions. The performance modeling methodology proves to be quite accurate in characterizing the effects of application and background load imbalance. The performance modeling methodology enables us to find the optimal set of processors to use for a synchronous iterative application running on a network, which is important given the emergence of systems that service batches of applications on networked resources. Various policies for the use of the workstations are then considered and the optimal (or near-optimal) scheduling found. Policy choices we investigate include determining the computational costs for individual workstations and relating the priority of background load usage versus the parallel application. We also consider basing costs on time, loading conditions, or other factors. We discuss related problems, such as load balancing, for improving the efficiency with which we utilize networked resources. The accurate performance modelling methodology provides significant help in addressing these and similar issues.