Genetic programming II (videotape): the next generation
Genetic programming II (videotape): the next generation
Using MPI: portable parallel programming with the message-passing interface
Using MPI: portable parallel programming with the message-passing interface
Interpretive performance prediction for parallel application development
Journal of Parallel and Distributed Computing
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
An empirical performance evaluation of scalable scientific applications
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
GYRO: A 5-D Gyrokinetic-Maxwell Solver
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Early Evaluation of the Cray X1
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
A framework to develop symbolic performance models of parallel applications
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Automatic performance analysis of message passing applications using the KappaPI 2 tool
PVM/MPI'05 Proceedings of the 12th European PVM/MPI users' group conference on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Automatic tuning of PDGEMM towards optimal performance
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
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Performance models of high performance computing (HPC) applications are important for several reasons. First, they provide insight to designers of HPC systems on the role of subsystems such as the processor or the network in determining application performance. Second, they allow HPC centers more accurately to target procurements to resource requirements. Third, they can be used to identify application performance bottlenecks and to provide insights about scalability issues. The suitability of a performance model, however, for a particular performance investigation is a function of both the accuracy and the cost of the model. A semi-empirical model previously published by the authors for an astrophysics application was shown to be inaccurate when predicting communication cost for large numbers of processors. It is hypothesized that this deficiency is due to the inability of the model adequately to capture communication contention (threshold effects) as well as other unmodeled components such as noise and I/O contention. In this paper we present a new approach to capture these unknown features to improve the predictive capabilities of the model. This approach uses a systematic model error-correction procedure that uses evolutionary algorithms to find an error correction term to augment the eXisting model. Four variations of this procedure were investigated and all were shown to produce better results than the original model. Successful cross-platform application of this approach showed that it adequately captures machine dependent characteristics. This approach was then successfully demonstrated for a second application, further showing its versatility.