Applied multivariate statistical analysis
Applied multivariate statistical analysis
Analysis of branch prediction via data compression
Proceedings of the seventh international conference on Architectural support for programming languages and operating systems
The SimpleScalar tool set, version 2.0
ACM SIGARCH Computer Architecture News
Proceedings of the 33rd annual ACM/IEEE international symposium on Microarchitecture
Managing multi-configuration hardware via dynamic working set analysis
ISCA '02 Proceedings of the 29th annual international symposium on Computer architecture
Automatically characterizing large scale program behavior
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Basic Block Distribution Analysis to Find Periodic Behavior and Simulation Points in Applications
Proceedings of the 2001 International Conference on Parallel Architectures and Compilation Techniques
Workload Design: Selecting Representative Program-Input Pairs
Proceedings of the 2002 International Conference on Parallel Architectures and Compilation Techniques
Positional adaptation of processors: application to energy reduction
Proceedings of the 30th annual international symposium on Computer architecture
Proceedings of the 30th annual international symposium on Computer architecture
Characterizing and Predicting Program Behavior and its Variability
Proceedings of the 12th International Conference on Parallel Architectures and Compilation Techniques
Picking Statistically Valid and Early Simulation Points
Proceedings of the 12th International Conference on Parallel Architectures and Compilation Techniques
ASPLOS XI Proceedings of the 11th international conference on Architectural support for programming languages and operating systems
Method-level phase behavior in java workloads
OOPSLA '04 Proceedings of the 19th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Pinpointing Representative Portions of Large Intel® Itanium® Programs with Dynamic Instrumentation
Proceedings of the 37th annual IEEE/ACM International Symposium on Microarchitecture
Transition Phase Classification and Prediction
HPCA '05 Proceedings of the 11th International Symposium on High-Performance Computer Architecture
Proceedings of the international symposium on Code generation and optimization
Selecting Software Phase Markers with Code Structure Analysis
Proceedings of the International Symposium on Code Generation and Optimization
Wavelet-based phase classification
Proceedings of the 15th international conference on Parallel architectures and compilation techniques
Complexity-based program phase analysis and classification
Proceedings of the 15th international conference on Parallel architectures and compilation techniques
Structures for phase classification
ISPASS '04 Proceedings of the 2004 IEEE International Symposium on Performance Analysis of Systems and Software
Motivation for Variable Length Intervals and Hierarchical Phase Behavior
ISPASS '05 Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software, 2005
The Strong correlation Between Code Signatures and Performance
ISPASS '05 Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software, 2005
A detailed study on phase predictors
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
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It is well known that a program exhibits time-varying execution behavior, i.e., a program typically goes through a number of phases during its execution exhibiting relatively homogeneous behavior within a phase and distinct behavior across phases. In fact, several recent research studies have been exploiting this time-varying behavior for various purposes such as simulation acceleration, code optimization, hardware adaptation for reducing energy consumption, etc. This paper proposes phase complexity surfaces to characterize a computer program's phase behavior across various time scales in an intuitive manner. The phase complexity surfaces incorporate metrics that characterize phase behavior in terms of the number of phases, their predictability, the degree of variability within and across phases, and the phase behavior's dependence on the time scale granularity. Leveraging phase complexity surfaces, the paper then characterizes the phase behavior of the SPEC CPU benchmarks across multiple platforms (Alpha and IA-32) and across two CPU benchmark suite generations (CPU2000 and CPU2006).