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PLDI '95 Proceedings of the ACM SIGPLAN 1995 conference on Programming language design and implementation
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PLDI '96 Proceedings of the ACM SIGPLAN 1996 conference on Programming language design and implementation
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Proceedings of the ACM SIGPLAN 1997 conference on Programming language design and implementation
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PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
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PPoPP '01 Proceedings of the eighth ACM SIGPLAN symposium on Principles and practices of parallel programming
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SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
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IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
A framework for performance modeling and prediction
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
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Proceedings of the 2002 ACM/IEEE conference on Supercomputing
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Proceedings of the 2002 ACM/IEEE conference on Supercomputing
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Proceedings of the 2002 ACM/IEEE conference on Supercomputing
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WWC '01 Proceedings of the Workload Characterization, 2001. WWC-4. 2001 IEEE International Workshop
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ACM Computing Surveys (CSUR)
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SCOPES '08 Proceedings of the 11th international workshop on Software & compilers for embedded systems
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ACM Transactions on Embedded Computing Systems (TECS)
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Performance understanding and prediction are extremely important goals for guiding the application of program optimizations or in helping programmers focus their efforts when tuning their applications. In this paper we survey current approaches in performance understanding and modeling for high-performance scientific applications. We also describe a performance modeling and prediction approach that relies on the synergistic collaboration of compiler analysis, compiler-generated instrumentation (to observe relevant run-time input values) and multimodel performance modeling. A compiler analyzes the source code to derive a discrete set of parameterizable performance models. The models use run-time data to define the values of their parameters. This approach, we believe, will allow for higher performance modeling accuracy and more importantly to more precise identification of what the causes of performance problems are.