Future Generation Computer Systems - Special issue on metacomputing
Automatically tuned collective communications
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Statistical Models for Automatic Performance Tuning
ICCS '01 Proceedings of the International Conference on Computational Sciences-Part I
Learning to Predict Performance from Formula Modeling and Training Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The LAPACK for Clusters Project: An Example of Self Adapting Numerical Software
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 9 - Volume 9
Architecture of an automatically tuned linear algebra library
Parallel Computing
Parallel Computing - Heterogeneous computing
ABCLib_DRSSED: A parallel eigensolver with an auto-tuning facility
Parallel Computing
Building the functional performance model of a processor
Proceedings of the 2006 ACM symposium on Applied computing
d-spline based incremental parameter estimation in automatic performance tuning
PARA'06 Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
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The performance of parallel linear algebra routines can be improved automatically using different methods. Our technique is based on the modellisation of the execution time of each routine, using information generated by routines from lower levels. However, sometimes the information generated at one level is not accurate enough to be used satisfactorily at higher levels. Therefore, a remodelling of the routines is performed by using (applied appropriately) polynomial regression. A remodelling phase is proposed, and analysed with a parallel matrix multiplication.