Experiments in Automated Load Balancing.
INSTITUTE FOR COMPUTER APPLICATIONS IN SCIENCE AND ENGINEERING HAMPTON VA
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One of the promises of parallelized discrete-event simulation is that it might provide significant speedups over sequential simulation. In reality, high performance cannot be achieved unless the system is fine-tuned to balance computation, communication, and synchronization requirements. As a result, parallel discrete-event simulation needs tools to automate the tuning process with little or no modification to the users simulation code. In this paper, we discuss our experiments in automated load balancing using the SPEEDES simulation framework. Specifically, we examine three mapping algorithms that use run-time measurements. Using simulation models of queuing networks and the National Airspace System, we investigate i the use of run-time data to guide mapping, ii the utility of considering communication costs in a mapping algorithm, iii the degree to which computational hot-spots ought to be broken up in the linearization, and iv the relative execution costs of the different algorithms.
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