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The performance of computationally intensive scientific applications on the underlying system can be maximized by providing application-level load balancing of loop iterates via the use of dynamic loop scheduling (DLS) algorithms. These DLS methods are based on probabilistic analyses, and therefore account for unpredictable variations of algorithmic, systemic and application level characteristics. A considerable number of DLS algorithms has been proposed in the last decade, and some of them have been effectively integrated into scientific and engineering applications, yielding significant performance improvements. However, scheduling scientific applications in large-scale distributed systems where, the chances of failure, such as processor or link failure, are high, makes the problem of achieving a load balanced execution even more challenging. Although real experiments are necessary to verify the benefits of using DLS, they prove to be very time consuming when every level of detail is required for the assessment of the execution of complex, data parallel and irregular scientific applications using DLS on a wide range of architectural platforms and computational environments. Thus, we propose the use of simulators which can give results that are not always experimentally measurable with the current technology. Simulations also help in studying the problem at various levels of abstraction and provide practical feedback. In this paper, we discuss the implementation of DLS techniques in Alea, a Grid Sim based scheduling simulator. Based on the simulation results, we further compare the load balancing characteristics of these methods in a simulated parallel and distributed computing environment.
Date of Conference: 6-8 July 2011