Automatically characterizing large scale program behavior
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Reducing State Loss For Effective Trace Sampling of Superscalar Processors
ICCD '96 Proceedings of the 1996 International Conference on Computer Design, VLSI in Computers and Processors
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
Using SimPoint for accurate and efficient simulation
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
SMARTS: accelerating microarchitecture simulation via rigorous statistical sampling
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
Characterizing and Comparing Prevailing Simulation Techniques
HPCA '05 Proceedings of the 11th International Symposium on High-Performance Computer Architecture
MinneSPEC: A New SPEC Benchmark Workload for Simulation-Based Computer Architecture Research
IEEE Computer Architecture Letters
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Software simulators remain several orders of magnitude slower than the modern microprocessor architectures they simulate. Although various reduced-time simulation tools are available to accurately help pick truncated benchmark simulation, they either come with a need for offline analysis of the benchmarks initially or require many iterative runs of the benchmark. In this paper, we present a novel sampling simulation method, which only requires a single run of the benchmark to achieve a desired confidence interval, with no offline analysis and gives comparable results in accuracy and sample sizes to current simulation methodologies. Our method is a novel configuration independent approach that incorporates an Autoregressive (AR) model using the squared coefficient of variance (SCV) of Cycles per Instruction (CPI). Using the sampled SCVs of past intervals of a benchmark, the model computes the required number of samples for the next interval through a derived relationship between number of samples and the SCVs of the CPI distribution. Our implementation of the AR model achieves an actual average error of only 0.76% on CPI with a 99.7% confidence interval of ±0.3% for all SPEC2K benchmarks while simulating, in detail, an average of 40 million instructions per benchmark.