A hardware-driven profiling scheme for identifying program hot spots to support runtime optimization
ISCA '99 Proceedings of the 26th annual international symposium on Computer architecture
Dynamo: a transparent dynamic optimization system
PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
Managing multi-configuration hardware via dynamic working set analysis
ISCA '02 Proceedings of the 29th annual international symposium on Computer architecture
Introduction to Algorithms
Automatically characterizing large scale program behavior
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
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
An infrastructure for adaptive dynamic optimization
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
Continuous program optimization: A case study
ACM Transactions on Programming Languages and Systems (TOPLAS)
Proceedings of the 30th annual international symposium on Computer architecture
Comparing Program Phase Detection Techniques
Proceedings of the 36th annual IEEE/ACM International Symposium on Microarchitecture
Prefetch injection based on hardware monitoring and object metadata
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation
Concurrency and Computation: Practice & Experience - 2002 ACM Java Grande—ISCOPE Conference Part I
Dynamic Helper Threaded Prefetching on the Sun UltraSPARC CMP Processor
Proceedings of the 38th annual IEEE/ACM International Symposium on Microarchitecture
Online Phase Detection Algorithms
Proceedings of the International Symposium on Code Generation and Optimization
Dynamic code region (DCR) based program phase tracking and prediction for dynamic optimizations
HiPEAC'05 Proceedings of the First international conference on High Performance Embedded Architectures and Compilers
Online Phase Detection Algorithms
Proceedings of the International Symposium on Code Generation and Optimization
Wavelet-based phase classification
Proceedings of the 15th international conference on Parallel architectures and compilation techniques
Effective management of multiple configurable units using dynamic optimization
ACM Transactions on Architecture and Code Optimization (TACO)
Phase detection using trace compilation
PPPJ '09 Proceedings of the 7th International Conference on Principles and Practice of Programming in Java
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Dynamic optimization relies on phase detection for two important functions (1) To detect change in code working set and (2) To detect change in performance characteristics that can affect optimization strategy. Current prototype runtime optimization systems [12][13] compare aggregate metrics like CPI over fixed time intervals to detect a change in working set and a change in performance. While simple and cost-effective, these metrics are sensitive to sampling rate and interval size. A phase detection scheme that computes performance metrics by aggregating the performance of individually optimized regions can be misled by some regions impacting aggregate metrics adversely. In this paper, we investigate the benefits and limitations of using aggregate metrics for phase detection, which we call Global Phase Detection (GPD). We present a new model to detect change in working set and propose that the scope of phase detection be limited to within the candidate regions for optimization. By associating phase detection to individual regions we can isolate the effects of regions that are inherently unstable. This approach, which we call Local Phase Detection (LPD), shows improved performance on several benchmarks even when global phase detection is not able to detect stable phases.