Predicting the performance of general task

The Glamis methodology provides a framework for performance modelling of parallel applications, based on a combination of task graph models and queueing network models. This paper presents a new, moderately cheap algorithm which enables the analysis of arbitrary task graph models of parallel programs with general precedence relationships, given a queueing model of the underlying machine. The algorithm gen-eralises over an earlier algorithm. An extension to this algorithm is proposed to allow for the analysis of a certain type of mutual exclusion at the task level. The features of the methodology are illustrated by means of a case study, modelling a task graph executor running on a distributed-memory parallel machine. Measurements carried out on the actual machine, as well as simulations, are used to validate the predictions.