Future Generation Computer Systems - Special issue on metacomputing
A first order approximation to the optimum checkpoint interval
Communications of the ACM
Predictive performance and scalability modeling of a large-scale application
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Application Execution Steering using On-the-Fly Performance Prediction
HPCN Europe 1998 Proceedings of the International Conference and Exhibition on High-Performance Computing and Networking
Adaptive Computing on the Grid Using AppLeS
IEEE Transactions on Parallel and Distributed Systems
Performance Prediction in Production Environments
IPPS '98 Proceedings of the 12th. International Parallel Processing Symposium on International Parallel Processing Symposium
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Pace--A Toolset for the Performance Prediction of Parallel and Distributed Systems
International Journal of High Performance Computing Applications
Concurrency and Computation: Practice & Experience - The High Performance Architectural Challenge: Mass Market versus Proprietary Components?
International Journal of High Performance Computing Applications
A General Performance Model of Structured and Unstructured Mesh Particle Transport Computations
The Journal of Supercomputing
A performance model of non-deterministic particle transport on large-scale systems
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartIII
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While it is possible to accurately predict the execution time of a given iteration of an adaptive application, it is not generally possible to predict the data-dependent adaptive behavior the application will take and therefore to predict the total execution time for a given execution. To remedy this situation we have developed an executable performance model that can be utilized dynamically at runtime directly from the application of interest. In this manner, the application itself can rapidly predict the expected execution time for its next iteration based on current information on the data layout and level of adaptivity. This enables the application itself to determine: if an optimum level of performance is being achieved (i.e. by comparing measured and predicted times); when to perform a checkpoint (if the next iteration will exceed a predefined time limit between checkpoints); or when to terminate (if the next iteration will exceed the application's system time allocation for instance). The dynamic model is shown to have high accuracy over a number of test cases, even in the presence of interference (system activities that are not a part of application activities).