Characterizing and predicting program behavior and its variability

Duesterwald, Evelyn; Caşcaval, Călin; Dwarkadas, Sandhya

doi:10.1109/pact.2003.1238018

Cited by 138 publications

(116 citation statements)

References 33 publications

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“…Finally, the repetitive behavior of applications has been exploited in speeding up architectural simulators [22], predicting performance metrics based on history information [12] and synthesizing kernels that resemble applications using architectural simulation [5]. Such studies exploit repetition at "instruction block" level, while we exploit larger-scale and more explicit behavior repetition in high performance scientific codes, based on the iterative nature many of them possess.…”

Section: Related Workmentioning

confidence: 99%

Cross-Platform Performance Prediction of Parallel Applications Using Partial Execution

Yang

Mueller

ACM/IEEE SC 2005 Conference (SC'05)

113

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Performance prediction across platforms is increasingly important as developers can choose from a wide range of execution platforms. The main challenge remains to perform accurate predictions at a low-cost across different architectures.In this paper, we derive an affordable method approaching cross-platform performance translation based on relative performance between two platforms. We argue that relative performance can be observed without running a parallel application in full. We show that it suffices to observe very short partial executions of an application since most parallel codes are iterative and behave predictably manner after a minimal startup period. This novel prediction approach is observation-based. It does not require program modeling, code analysis, or architectural simulation. Our performance results using real platforms and production codes demonstrate that prediction derived from partial executions can yield high accuracy at a low cost. We also assess the limitations of our model and identify future research directions on observationbased performance prediction.

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Section: Related Workmentioning

confidence: 99%

Cross-Platform Performance Prediction of Parallel Applications Using Partial Execution

Yang

Mueller

ACM/IEEE SC 2005 Conference (SC'05)

113

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“…We use signal analysis techniques in a similar fashion compared to prior work [9], [12] to allow us to capture micro-architecture independent application characteristics.…”

Section: B Determining Application Periodicitymentioning

confidence: 99%

Sampled simulation of multi-threaded applications

Carlson

Heirman

Eeckhout

2013

2013 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)

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Abstract-Sampling is a well-known workload reduction technique that allows one to speed up architectural simulation while accurately predicting performance. Previous sampling methods have been shown to accurately predict single-threaded application runtime based on its overall IPC. However, these previous approaches are unsuitable for general multi-threaded applications, for which IPC is not a good proxy for runtime. Additionally, we find that issues such as application periodicity and inter-thread synchronization play a significant role in determining how best to sample these applications.The proposed multi-threaded application sampling methodology is able to derive an effective sampling strategy for candidate applications using architecture-independent metrics. Using this methodology, large input sets can now be simulated which would otherwise be infeasible, allowing for more accurate conclusions to be made than from studies using scaled-down input sets. Through the use of the proposed methodology, we can simulate less than 10% of the total application runtime in detail. On the SPEComp, NPB and PARSEC benchmarks, running on an 8-core simulated system, we achieve an average absolute error of 3.5%.

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“…This fingerprint depends on the executed source code. Some methods depend on run-time event counters or other metrics [2,3,17,9], such as IPC, power, cache misses rate and branch misprediction, to identify phases. Our infrastructure uses the edge vector of each interval, a vector that gives a count for each control-flow edge in the program, along with the measured IPC.…”

Section: Related Workmentioning

confidence: 99%

Efficient Program Power Behavior Characterization

Jiménez

Kremer

2007

High Performance Embedded Architectures and Compilers

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Abstract. Fine-grained program power behavior is useful in both evaluating power optimizations and observing power optimization opportunities. Detailed power simulation is time consuming and often inaccurate. Physical power measurement is faster and objective. However, fine-grained measurement generates enormous amounts of data in which locating important features is difficult, while coarse-grained measurement sacrifices important detail. We present a program power behavior characterization infrastructure that identifies program phases, selects a representative interval of execution for each phase, and instruments the program to enable precise power measurement of these intervals to get their time-dependent power behavior. We show that the representative intervals accurately model the fine-grained time-dependent behavior of the program. They also accurately estimate the total energy of a program. Our compiler infrastructure allows for easy mapping between a measurement result and its corresponding source code. We improve the accuracy of our technique over previous work by using edge vectors, i.e., counts of traversals of control-flow edges, instead of basic block vectors, as well as incorporating event counters into our phase classification. We validate our infrastructure through the physical power measurement of 10 SPEC CPU 2000 integer benchmarks on an Intel Pentium 4 system. We show that using edge vectors reduces the error of estimating total program energy by 35% over using basic block vectors, and using edge vectors plus event counters reduces the error of estimating the fine-grained time-dependent power profile by 22% over using basic block vectors.

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Characterizing and predicting program behavior and its variability

Abstract: To reach the next level of performance and energy efficiency, optimizations

Cited by 138 publications

References 33 publications

Cross-Platform Performance Prediction of Parallel Applications Using Partial Execution

Cross-Platform Performance Prediction of Parallel Applications Using Partial Execution

Sampled simulation of multi-threaded applications

Efficient Program Power Behavior Characterization

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