Using MPI: portable parallel programming with the message-passing interface
Using MPI: portable parallel programming with the message-passing interface
Prophesy: Automating the Modeling Process
AMS '01 Proceedings of the Third Annual International Workshop on Active Middleware Services
Analysis of Benchmark Characteristics and Benchmark Performance
Analysis of Benchmark Characteristics and Benchmark Performance
Measuring Cache and TLB Performance and Their Effect of Benchmark Run
Measuring Cache and TLB Performance and Their Effect of Benchmark Run
Prophesy: an infrastructure for performance analysis and modeling of parallel and grid applications
ACM SIGMETRICS Performance Evaluation Review
GrADSolve: a grid-based RPC system for parallel computing with application-level scheduling
Journal of Parallel and Distributed Computing - Special issue on middleware
Grid harvest service: a performance system of grid computing
Journal of Parallel and Distributed Computing
Pegasus: A framework for mapping complex scientific workflows onto distributed systems
Scientific Programming
Using Templates to Predict Execution Time of Scientific Workflow Applications in the Grid
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
A Hybrid Intelligent Method for Performance Modeling and Prediction of Workflow Activities in Grids
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Predicting the execution time of grid workflow applications through local learning
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Future Generation Computer Systems
MuMMI: multiple metrics modeling infrastructure for exploring performance and power modeling
Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery
A framework for dynamically generating predictive models of workflow execution
WORKS '13 Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science
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Performance models provide significant insight into the performance relationships between an application and the system used for execution. The major obstacle to developing performance models is the lack of knowledge about the performance relationships between the different functions that compose an application. This paper addresses the issue by using a coupling parameter, which quantifies the interaction between kernels, to develop performance predictions. The results, using three NAS Parallel Application Benchmarks, indicate that thepredictions using the coupling parameter were greatly improved over a traditional technique of summing the execution times of the individual kernels in an application. In one case the coupling predictor had less than 1% relative error in contrast the summation methodology that had over 20% relative error. Further, as the problem size and number of processors scale, thecoupling values go through a finite number of major value changes that is dependent on the memory subsystem of the processor architecture.