Workload Characterization: Motivation, Goals and Methodology
WWC '98 Proceedings of the Workload Characterization: Methodology and Case Studies
Measuring Benchmark Similarity Using Inherent Program Characteristics
IEEE Transactions on Computers
Measuring Program Similarity: Experiments with SPEC CPU Benchmark Suites
ISPASS '05 Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software, 2005
SPEC CPU suite growth: an historical perspective
ACM SIGARCH Computer Architecture News
Performance counters and development of SPEC CPU2006
ACM SIGARCH Computer Architecture News
Decomposable and responsive power models for multicore processors using performance counters
Proceedings of the 24th ACM International Conference on Supercomputing
SubsetTrio: An evolutionary, geometric, and statistical benchmark subsetting framework
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Pruning hardware evaluation space via correlation-driven application similarity analysis
Proceedings of the 8th ACM International Conference on Computing Frontiers
Energy accounting for shared virtualized environments under DVFS using PMC-based power models
Future Generation Computer Systems
ACM Transactions on Architecture and Code Optimization (TACO)
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On August 24, 2006, the Standard Performance Evaluation Corporation (SPEC) announced CPU2006 -- the next generation of industry-standardized CPU-intensive benchmark suite. The SPEC CPU benchmark suite has become the most frequently used suite for simulation-based computer architecture research. Detailed processor simulators take days to weeks to simulate each of the SPEC CPU programs. In order to reduce simulation to a tractable time, architects and researchers often use only a subset of benchmarks from the SPEC CPU suite to evaluate the potential of their ideas. Prior research has demonstrated that statistical techniques are most effective to find a representative subset of benchmark programs from a benchmark suite. The objective of this paper is to apply multivariate statistical data analysis techniques for selecting a representative subset of programs from the SPEC CPU2006 benchmark suite. We measure a set of performance counter based characteristics for the SPEC CPU2006 programs across a large number of architectures and apply multivariate statistical analysis techniques to find a representative subset of benchmarks and representative input sets wherever multiple input sets are provided. The results from this paper will help architects and researchers to find a smaller but representative set of programs from the SPEC CPU2006 benchmark suite, when time or resource constraints prohibit experimentation with the entire benchmark suite.