ACM Transactions on Computer Systems (TOCS)
A Model of Workloads and its Use in Miss-Rate Prediction for Fully Associative Caches
IEEE Transactions on Computers
Expected I-cache miss rates via the gap model
ISCA '94 Proceedings of the 21st annual international symposium on Computer architecture
Cache miss equations: an analytical representation of cache misses
ICS '97 Proceedings of the 11th international conference on Supercomputing
Understanding some simple processor-performance limits
IBM Journal of Research and Development - Special issue: performance analysis and its impact on design
Cache performance analysis of traversals and random accesses
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Analytical cache models with applications to cache partitioning
ICS '01 Proceedings of the 15th international conference on Supercomputing
Automatic Code Mapping on an Intelligent Memory Architecture
IEEE Transactions on Computers
SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
Towards a theory of cache-efficient algorithms
Journal of the ACM (JACM)
A Novel Methodology Using Genetic Algorithms for the Design of Caches and Cache Replacement Policy
Proceedings of the 5th International Conference on Genetic Algorithms
Estimating cache misses and locality using stack distances
ICS '03 Proceedings of the 17th annual international conference on Supercomputing
A New Memory Monitoring Scheme for Memory-Aware Scheduling and Partitioning
HPCA '02 Proceedings of the 8th International Symposium on High-Performance Computer Architecture
Performance evaluation of cache replacement policies for the SPEC CPU2000 benchmark suite
ACM-SE 42 Proceedings of the 42nd annual Southeast regional conference
Predicting Inter-Thread Cache Contention on a Chip Multi-Processor Architecture
HPCA '05 Proceedings of the 11th International Symposium on High-Performance Computer Architecture
Using Prime Numbers for Cache Indexing to Eliminate Conflict Misses
HPCA '04 Proceedings of the 10th International Symposium on High Performance Computer Architecture
StatCache: a probabilistic approach to efficient and accurate data locality analysis
ISPASS '04 Proceedings of the 2004 IEEE International Symposium on Performance Analysis of Systems and Software
Adaptive insertion policies for high performance caching
Proceedings of the 34th annual international symposium on Computer architecture
Characterizing and modeling the behavior of context switch misses
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
RapidMRC: approximating L2 miss rate curves on commodity systems for online optimizations
Proceedings of the 14th international conference on Architectural support for programming languages and operating systems
Enhancing operating system support for multicore processors by using hardware performance monitoring
ACM SIGOPS Operating Systems Review
A mechanistic performance model for superscalar out-of-order processors
ACM Transactions on Computer Systems (TOCS)
Understanding the behavior and implications of context switch misses
ACM Transactions on Architecture and Code Optimization (TACO)
An efficient simulation algorithm for cache of random replacement policy
NPC'10 Proceedings of the 2010 IFIP international conference on Network and parallel computing
Brief announcement: paging for multicore processors
Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures
W-Order scan: minimizing cache pollution by application software level cache management for MMDB
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Optimizing integrated application performance with cache-aware metascheduling
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part II
The gradient-based cache partitioning algorithm
ACM Transactions on Architecture and Code Optimization (TACO) - HIPEAC Papers
Region scheduling: efficiently using the cache architectures via page-level affinity
ASPLOS XVII Proceedings of the seventeenth international conference on Architectural Support for Programming Languages and Operating Systems
Survey of scheduling techniques for addressing shared resources in multicore processors
ACM Computing Surveys (CSUR)
An empirical model for predicting cross-core performance interference on multicore processors
PACT '13 Proceedings of the 22nd international conference on Parallel architectures and compilation techniques
Estimating instantaneous cache hit ratio using Markov chain analysis
IEEE/ACM Transactions on Networking (TON)
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Due to the increasing gap between CPU and memory speed, cache performance plays an increasingly critical role in determining the overall performance of microprocessor systems. One of the important factors that a affect cache performance is the cache replacement policy. Despite the importance, current analytical cache performance models ignore the impact of cache replacement policies on cache performance. To the best of our knowledge, this paper is the first to propose an analytical model which predicts the performance of cache replacement policies. The input to our model is a simple circular sequence profiling of each application, which requires very little storage overhead. The output of the model is the predicted miss rates of an application under different replacement policies. The model is based on probability theory and utilizes Markov processes to compute each cache access' miss probability. The model realistic assumptions and relies solely on the statistical properties of the application, without relying on heuristics or rules of thumbs. The model's run time is less than 0.1 seconds, much lower than that of trace simulations. We validate the model by comparing the predicted miss rates of seventeen Spec2000 and NAS benchmark applications against miss rates obtained by detailed execution-driven simulations, across a range of different cache sizes, associativities, and four replacement policies, and show that the model is very accurate. The model's average prediction error is 1.41%,and there are only 14 out of 952 validation points in which the prediction errors are larger than 10%.