Quantitative system performance: computer system analysis using queueing network models
Quantitative system performance: computer system analysis using queueing network models
Software performance modelling using PEPA nets
WOSP '04 Proceedings of the 4th international workshop on Software and performance
Performance modeling from software components
WOSP '04 Proceedings of the 4th international workshop on Software and performance
Low-overhead memory leak detection using adaptive statistical profiling
ASPLOS XI Proceedings of the 11th international conference on Architectural support for programming languages and operating systems
Pace--A Toolset for the Performance Prediction of Parallel and Distributed Systems
International Journal of High Performance Computing Applications
Automatic generation of layered queuing software performance models from commonly available traces
Proceedings of the 5th international workshop on Software and performance
Resource Allocation for Autonomic Data Centers using Analytic Performance Models
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Continuous resource monitoring for self-predicting DBMS
MASCOTS '05 Proceedings of the 13th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
Performance modeling and prediction of enterprise JavaBeans with layered queuing network templates
SAVCBS '05 Proceedings of the 2005 conference on Specification and verification of component-based systems
Towards self-predicting systems: What if you could ask ‘what-if’?
The Knowledge Engineering Review
Methods of inference and learning for performance modeling of parallel applications
Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming
Predicting parallel application performance via machine learning approaches: Research Articles
Concurrency and Computation: Practice & Experience - Parallel and Distributed Computing (EuroPar 2005)
Performance prediction for a code with data-dependent runtimes
Concurrency and Computation: Practice & Experience - Selected Papers from the 2005 U.K. e-Science All Hands Meeting (AHM 2005)
A framework for measurement based performance modeling
WOSP '08 Proceedings of the 7th international workshop on Software and performance
Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Practical performance models for complex, popular applications
Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Hierarchical performance measurement and modeling of the linux file system
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Towards software performance engineering for multicore and manycore systems
ACM SIGMETRICS Performance Evaluation Review
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Predicting the performance of a computer program facilitates its efficient design, deployment, and problem detection. However, predicting performance of multithreaded programs is complicated by complex locking behavior and concurrent usage of computational resources. Existing performance models either require running the program in many different configurations or impose restrictions on the types of programs that can be modeled. This paper presents our approach towards building performance models that do not require vast amounts of training data. Our models are built using a combination of queuing networks and probabilistic call graphs. All necessary information is collected using static and dynamic analyses of a single run of the program. In our experiments these models were able to accurately predict performance of different types of multithreaded programs and detected those configurations that result in the programs' high performance.