Analytic evaluation of shared-memory systems with ILP processors
Proceedings of the 25th annual international symposium on Computer architecture
A Statistically Rigorous Approach for Improving Simulation Methodology
HPCA '03 Proceedings of the 9th International Symposium on High-Performance Computer Architecture
Workload Characterization: Motivation, Goals and Methodology
WWC '98 Proceedings of the Workload Characterization: Methodology and Case Studies
Microarchitecture Optimizations for Exploiting Memory-Level Parallelism
Proceedings of the 31st annual international symposium on Computer architecture
Comprehensive multiprocessor cache miss rate generation using multivariate models
ACM Transactions on Computer Systems (TOCS)
Performance prediction based on inherent program similarity
Proceedings of the 15th international conference on Parallel architectures and compilation techniques
Accurate and efficient regression modeling for microarchitectural performance and power prediction
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
A Predictive Performance Model for Superscalar Processors
Proceedings of the 39th Annual IEEE/ACM International Symposium on Microarchitecture
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
A performance methodology for commercial servers
IBM Journal of Research and Development
Quantifying hardware counter sampling error in computer system workload characterization
Quantifying hardware counter sampling error in computer system workload characterization
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Computer system design studies traditionally involve only small collections of benchmarks. Detailed benchmark analysis is extremely time consuming and requires a large amount of human and machine resources. Therefore, it is essential that the benchmark collection be representative of the customer workloads for which an architecture is developed. In recent work, interworkload distances have been proposed as a way of characterizing workload similarity. These distances are based on measurable/computable program characteristics, such as instruction mix or dependence distance. In the literature, these characteristics enter the distances symmetrically. We observe that the program behavior impact of different characteristics varies significantly. We propose a method of estimating the program behavior impact via a regression model. Its components then enter the distance definition directly, thus emphasizing high-impact characteristics. We also propose a data collection methodology that can be deployed at a customer site without requiring code instrumentation and/or a detailed simulation setup. We build a dataset consisting of 84 program characteristics for each of the 106 workloads and apply the proposed distance methodology to it.