Model-Based Performance Prediction in Software Development: A Survey
IEEE Transactions on Software Engineering
Predicting Inter-Thread Cache Contention on a Chip Multi-Processor Architecture
HPCA '05 Proceedings of the 11th International Symposium on High-Performance Computer Architecture
Investigating Cache Parameters of x86 Family Processors
Proceedings of the 2009 SPEC Benchmark Workshop on Computer Performance Evaluation and Benchmarking
When Misses Differ: Investigating Impact of Cache Misses on Observed Performance
ICPADS '09 Proceedings of the 2009 15th International Conference on Parallel and Distributed Systems
Fast modeling of shared caches in multicore systems
Proceedings of the 6th International Conference on High Performance and Embedded Architectures and Compilers
Can linear approximation improve performance prediction ?
EPEW'11 Proceedings of the 8th European conference on Computer Performance Engineering
Towards software performance engineering for multicore and manycore systems
ACM SIGMETRICS Performance Evaluation Review
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Memory caches significantly improve the performance of workloads that have temporal and spatial locality by providing faster access to data. Current processor designs have multiple cores sharing a cache. To accurately model a workload performance and to improve system throughput by intelligently scheduling workloads on cores, we need to understand how sharing caches between workloads affects their data accesses. Past research has developed analytical models that estimate the cache behavior for combined workloads given the stack distance profiles describing these workloads. We extend this research by presenting an analytical model with contributions to accuracy and composability - our model makes fewer simplifying assumptions than earlier models, and its output is in the same format as its input, which is an important property for hierarchical composition during software performance modeling. To compare the accuracy of our analytical model with earlier models, we attempted to reproduce the reported accuracy of those models. This proved to be difficult. We provide additional insight into the major factors that influence analytical model accuracy.