On the inclusion properties for multi-level cache hierarchies
ISCA '88 Proceedings of the 15th Annual International Symposium on Computer architecture
ACM Transactions on Computer Systems (TOCS)
Performance bounds for distributed systems with workload variabilities and uncertainties
Parallel Computing - Special issue: distributed and parallel systems: environments and tools
Analytic evaluation of shared-memory systems with ILP processors
Proceedings of the 25th annual international symposium on Computer architecture
Input uncertainty: accounting for parameter uncertainty in simulation input modeling
Proceedings of the 33nd conference on Winter simulation
Reducing input parameter uncertainty for simulations
Proceedings of the 33nd conference on Winter simulation
Variability in Architectural Simulations of Multi-Threaded Workloads
HPCA '03 Proceedings of the 9th International Symposium on High-Performance Computer Architecture
A Statistically Rigorous Approach for Improving Simulation Methodology
HPCA '03 Proceedings of the 9th International Symposium on High-Performance Computer Architecture
Comprehensive multiprocessor cache miss rate generation using multivariate models
ACM Transactions on Computer Systems (TOCS)
A performance methodology for commercial servers
IBM Journal of Research and Development
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Computer system models provide detailed answers to system performance. For a given system configuration, a system model estimates the cycles per instruction that the system would incur while running a given workload. In addition, it estimates the proportion of time that is spent in different parts of the system and other related metrics such as bus utilizations. Consider those inputs to a system model that are estimated with uncertainty. Examples include cache miss rates that are obtained via trace-driven cache simulation and also sometimes by extrapolating beyond the simulation domain. Errors incurred during the measurement and fitting processes are propagated to the system model outputs. On the other hand, other inputs such as hardware latencies are known precisely. In this paper we propose several measures of uncertainty of system model outputs when it stems from uncertainty in the inputs. Some of these measures are based on sensitivity of an output to the inputs. We propose ways of defining and determining these sensitivities and turning them into uncertainty measures. Other measures are based on sampling schemes. Additionally, we determine uncertainty measures for the system model outputs over a wide range of inputs covering large system design spaces. This is done by first selecting a set of input configurations based on an experimental design methodology where the uncertainty measures are determined. Then these data are used to interpolate the uncertainty measure function over the rest of the input space. We quantitatively characterize each input's contribution to the output uncertainty over the input's entire range. We also propose ways that call attention to high output uncertainty regions in the input space. The methodology is illustrated on system models developed at Sun Microsystems Laboratories. The particular goal of the performance analysis is a design of level two caches.