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PADS '94 Proceedings of the eighth workshop on Parallel and distributed simulation
Software—Practice & Experience
Performance prediction of large parallel applications using parallel simulations
Proceedings of the seventh ACM SIGPLAN symposium on Principles and practice of parallel programming
DiP: A Parallel Program Development Environment
Euro-Par '96 Proceedings of the Second International Euro-Par Conference on Parallel Processing-Volume II
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Pace--A Toolset for the Performance Prediction of Parallel and Distributed Systems
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IEEE Transactions on Computers
Simulation-based performance prediction for large parallel machines
International Journal of Parallel Programming - Special issue: The next generation software program
A performance prediction framework for scientific applications
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartIII
Scalable parallel trace-based performance analysis
EuroPVM/MPI'06 Proceedings of the 13th European PVM/MPI User's Group conference on Recent advances in parallel virtual machine and message passing interface
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The increasing complexities of today's parallel systems pose new challenges for performance prediction. Effective performance prediction can provide insight, deepen understanding and further identify potential performance bottlenecks of system/application combinations. In this paper, we present and evaluate a multi-phase trace-driven (MPTD) performance prediction framework for parallel systems. In the trace generation phase, based on a relatively simple performance model, MPTD performs parallel performance simulation to generate primary prediction results and traces rapidly. In the trace adjustment phase, traces are transformed or re-simulated based on performance models of new component architecture or more detailed performance models. This phase is self-repeatable (it can be performed more than once and need not go back to the former phase) to enable more flexible reuse of traces. We implemented an instantiation of MPTD to predict the performance of popular multi-core cluster systems. Analysis and tests show that MPTD is scalable, flexible, and can help researchers for better balancing accuracy and efficiency of performance prediction.