Demonstrating the scalability of a molecular dynamics application on a Petaflop computer
ICS '01 Proceedings of the 15th international conference on Supercomputing
Predictive performance and scalability modeling of a large-scale application
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
A framework for performance modeling and prediction
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
A Portable Programming Interface for Performance Evaluation on Modern Processors
International Journal of High Performance Computing Applications
Cross-Platform Performance Prediction of Parallel Applications Using Partial Execution
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Blue Gene/L compute chip: memory and Ethernet subsystem
IBM Journal of Research and Development
A framework to develop symbolic performance models of parallel applications
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Early evaluation of the cray XT3
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
A regression-based approach to scalability prediction
Proceedings of the 22nd annual international conference on Supercomputing
An exploration of performance attributes for symbolic modeling of emerging processing devices
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
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Multi-resolution validation of hierarchical performance models of scientific applications is critical primarily for two reasons. First, the step-by-step validation determines the correctness of all essential components or phases in a science simulation. Second, a model that is validated at multiple resolution levels is the very first step to generate predictive performance models, for not only existing systems but also for emerging systems and future problem sizes. We present the design and validation of hierarchical performance models of two scientific benchmarks using a new technique called the modeling assertions (MA). Our MA prototype framework generates symbolic performance models that can be evaluated efficiently by generating the equivalent model representations in Octave and MATLAB. The multi-resolution modeling and validation is conducted on two contemporary, massively-parallel systems, XT3 and Blue Gene/L system. The workload distribution and the growth rates predictions generated by the MA models are confirmed by the experimental data collected on the MPP platforms. In addition, the physical memory requirements that are generated by the MA models are verified by the runtime values on the Blue Gene/L system, which has 512 MBytes and 256 MBytes physical memory capacity in its two unique execution modes.