Motivation for Variable Length Intervals and Hierarchical Phase Behavior

Lau, Jeremy; Perelman, Erez; Hamerly, Greg; Sherwood, Timothy; Calder, Brad

doi:10.1109/ispass.2005.1430568

Cited by 60 publications

(37 citation statements)

References 34 publications

(55 reference statements)

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“…The error rates are low and the simulation time saving is significant. A newer version of SimPoint supports variable length intervals [12]. That study shows a hierarchy of phase behavior in programs and the feasibility of variable length intervals in program phase classification.…”

Section: Related Workmentioning

confidence: 78%

“…Some of them use control-flow information [15,16,14,11,12], such as counts of executed instructions, basic blocks, loops, or functions, as the fingerprint of program execution. This fingerprint depends on the executed source code.…”

Section: Related Workmentioning

confidence: 99%

“…One is based on the percentage of the number of executed instructions of the corresponding phase in that of the whole program, the other is based on the number of intervals in the corresponding phase as the one used in [16]. A recent version of the SimPoint tool also supports variable-length phases [12].…”

Section: Phase Classification For Power Behavior Characterizationmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 78%

Section: Related Workmentioning

confidence: 99%

Section: Phase Classification For Power Behavior Characterizationmentioning

confidence: 99%

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Efficient Program Power Behavior Characterization

Jiménez

Kremer

2007

High Performance Embedded Architectures and Compilers

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“…Figures 1 and 2 respectively show the number of instructions and accuracy of different BeeRS and SimPoint configurations (the maximum number of samples is set to 50 for 10M intervals, and to 100 for 1M intervals, so as to provide a fair accuracy/size comparison with BeeRS). We use perfect warm-up for SimPoint as in most of the articles [12,10,6,5] (recall SimPoint treats sampling as an issue independent from warm-up). As mentioned in the introduction, while the accuracy of SimPoint 10M is barely sensitive to warm-up, SimPoint 1M becomes fairly sensitive (from 0.7% down to 2.4%), and the trend can only worsen as the sample size decreases.…”

Section: Discussionmentioning

confidence: 99%

“…While the recent surge of research articles on sampling started with rather large sample sizes (100M in the first SimPoint article [12]), it has later shifted to very small intervals (1,000 in SMARTS [14]), and it is now converging to intermediate sizes (1M and 10M in SimPoint [10,6]), and even to varying sizes in EXPERT [7] and SimPoint VLI [5] (ranging from 52,000 to 6.1M in EXPERT, and 100M to 500M in SimPoint VLI). With 100M samples, warm-up is not an issue, at least with current cache sizes.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Budgeted region sampling (BeeRS): do not separate sampling from warm-up, and then spend wisely your simulation budget

Pérez

Berry

Temam

2005

Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005.

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Abstract. While the recent surge of research articles on sampling started with rather large sample sizes, it has later shifted to very small intervals, and it is now converging to intermediate sizes, and even to varying sizes. With 100M samples, warm-up is not an issue, at least with current cache sizes. However, with significantly smaller samples, warm-up becomes critical, especially when the sampling target accuracy is of the order of a few percent. However, in most sampling research works, warm-up has largely been treated as a separate issue.In this article, we advocate for an integrated approach at (simulator-based) warm-up and sampling. Instead of separating warm-up and sampling, we take exactly the opposite approach, provide a common instruction budget for warm-up and sampling, and we attempt to spend it as wisely as possible on either one.This budget and integrated approach at warm-up and sampling achieves an average CPI error of 1.68% on the 26 Spec benchmarks with an average sampling size of 288 millions instructions, and at the same time, it relieves the user from any delicate decision such as setting the sampling or warm-up sizes, thanks to the integrated warm-up+sampling and the region partitioning approaches.

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Extracting the optimal sampling frequency of applications using spectral analysis

Casas

Servat

Badía

et al. 2011

Concurrency and Computation

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SUMMARYThe research community have agreed on several applications as benchmarks to evaluate the adequateness of architectures and high performance computing infrastructures. The performance of these benchmarks is used to determine the weaknesses and strengths of novel designs. Therefore, the performance evaluation of benchmarks is a key factor in the process of designing new architectures. In this paper, we propose a new method based on spectral analysis that allows to perform an automatic analysis of benchmarks' executions. The output of the new method is a representative segment of the benchmarks' executions. Given the nature of the method, the optimal sampling interval length of applications is obtained. This method complements and improves existing techniques focused on the reduction of the application's instruction execution stream of sequential benchmarks and enables the extraction of significant performance information of parallel benchmarks without executing the whole application. The results obtained with the SPEC CPU2000 and the NAS Parallel Benchmarks demonstrate the efficiency and benefits of the approach.

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Motivation for Variable Length Intervals and Hierarchical Phase Behavior

Abstract: Abstract

Cited by 60 publications

References 34 publications

Efficient Program Power Behavior Characterization

Efficient Program Power Behavior Characterization

Budgeted region sampling (BeeRS): do not separate sampling from warm-up, and then spend wisely your simulation budget

Extracting the optimal sampling frequency of applications using spectral analysis

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