Matrix analysis
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Graphoids: a qualitative framework for probabilistic inference
Graphoids: a qualitative framework for probabilistic inference
Elements of information theory
Elements of information theory
An introduction to Kolmogorov complexity and its applications
An introduction to Kolmogorov complexity and its applications
Software—Practice & Experience
Realistic parallel performance estimation
Parallel Computing - Special double issue on environment and tools for parallel scientific computing
Integrated visualization of parallel program performance data
Parallel Computing - Special double issue on environment and tools for parallel scientific computing
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
HiPC '02 Proceedings of the 9th International Conference on High Performance Computing
SCALEA: A Performance Analysis Tool for Distributed and Parallel Programs
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
A framework for performance modeling and prediction
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
A general identification condition for causal effects
Eighteenth national conference on Artificial intelligence
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
A Portable Programming Interface for Performance Evaluation on Modern Processors
International Journal of High Performance Computing Applications
How Well Can Simple Metrics Represent the Performance of HPC Applications?
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Modeling application performance by convolving machine signatures with application profiles
WWC '01 Proceedings of the Workload Characterization, 2001. WWC-4. 2001 IEEE International Workshop
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
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Causal modeling and the accompanying learning algorithms provide useful extensions for in-depth statistical investigation and automation of performance modeling. We enlarged the scope of existing causal structure learning algorithms by using the form-free information-theoretic concept of mutual information and by introducing the complexity criterion for selecting direct relations among equivalent relations. The underlying probability distribution of experimental data is estimated by kernel density estimation. We then reported on the benefits of a dependency analysis and the decompositional capacities of causal models. Useful qualitative models, providing insight into the role of every performance factor, were inferred from experimental data. This paper reports on the results for a LU decomposition algorithm and on the study of the parameter sensitivity of the Kakadu implementation of the JPEG-2000 standard. Next, the analysis was used to search for generic performance characteristics of the applications.