Predictive performance and scalability modeling of a large-scale application
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Predicting Application Run Times Using Historical Information
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Prophesy: an infrastructure for performance analysis and modeling of parallel and grid applications
ACM SIGMETRICS Performance Evaluation Review
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Cross-architecture performance predictions for scientific applications using parameterized models
Proceedings of the joint international conference on Measurement and modeling of computer systems
Minimizing development and maintenance costs in supporting persistently optimized BLAS
Software—Practice & Experience - Research Articles
Cross-Platform Performance Prediction of Parallel Applications Using Partial Execution
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Soft Benchmarks-Based Application Performance Prediction Using a Minimum Training Set
E-SCIENCE '06 Proceedings of the Second IEEE International Conference on e-Science and Grid Computing
Design and Analysis of Experiments
Design and Analysis of Experiments
ASKALON: A Grid Application Development and Computing Environment
GRID '05 Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing
A performance prediction framework for scientific applications
Future Generation Computer Systems
Performance modeling: understanding the past and predicting the future
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
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Automatic execution time prediction of the Grid applications plays a critical role in making the pervasive Grid more reliable and predictable. However, automatic execution time prediction has not been addressed due to the diversity of the Grid applications, usability of an application in multiple contexts, dynamic nature of the Grid, and concerns about result accuracy and time expensive experimental training. We introduce an optimized, low-cost, and efficient yet automatic training phase for automatic execution time prediction of Grid applications. Our approach is supported by intra - and inter-platform performance sharing and translation mechanisms. We are able to reduce the total number of experiments from an polynomial complexity to a linear complexity.