Discovery of Locality-Improving Refactorings by Reuse Path Analysis

Beyls, Kristof; D’Hollander, Erik H.

doi:10.1007/11847366_23

Cited by 52 publications

(41 citation statements)

References 12 publications

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“…Locality sampling: A representative system was developed by Beyls and D'Hollander [10]. It instruments a program to skip every k accesses and take the next address as a sample.…”

Section: Related Workmentioning

confidence: 99%

Cache Conscious Task Regrouping on Multicore Processors

Xiang

Bao

Ding

et al. 2012

2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Ccgrid 2012)

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Abstract-Because of the interference in the shared cache on multicore processors, the performance of a program can be severely affected by its co-running programs. If job scheduling does not consider how a group of tasks utilize cache, the performance may degrade significantly, and the degradation usually varies sizably and unpredictably from run to run.In this paper, we use trace-based program locality analysis and make it efficient enough for dynamic use. We show a complete on-line system for periodically measuring the parallel execution, predicting and ranking cache interference for all co-run choices, and reorganizing programs based on the prediction. We test our system on floating-point and mixed integer and floating-point workloads composed of SPEC 2006 benchmarks and compare with the default Linux job scheduler to show the benefit of the new system in improving performance and reducing performance variation.

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“…Locality sampling: A representative system was developed by Beyls and D'Hollander [10]. It instruments a program to skip every k accesses and take the next address as a sample.…”

Section: Related Workmentioning

confidence: 99%

Cache Conscious Task Regrouping on Multicore Processors

Xiang

Bao

Ding

et al. 2012

2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Ccgrid 2012)

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“…To reduce the cost, we have integrated the new model to a sampling-based tool -Suggestion of Locality Optimization (SLO), developed by Beyls and D'Hollander at Ghent University [4]. SLO uses reservoir sampling [14], which has two distinct properties.…”

Section: Sampling Analysismentioning

confidence: 99%

“…For sampling, we have integrated our spatial-locality analyzer into the SLO tool developed by Beyls and D'Hollander for temporal locality analysis [4]. We call the combined system SLOR.…”

Section: Sampling-based Toolmentioning

confidence: 99%

“…It is based on the GUI system of SLO, which displays temporal locality results including reuse paths and suggestions of computation transformation [4]. The temporal results are still retained under the tab "Temporal", as shown in the upper left corner of the screen shot in Figure 7.…”

Section: Sampling-based Toolmentioning

confidence: 99%

“…The sampling accuracy is checked using a statistical technique, the Hellinger affinity kernel [7]. Approximating reuse distance with time distance is used in the SLO tool [4] and systematically studied as a statistical problem [25]. Recent improvements including extension to arbitrary-scale histograms and implementation using the memory-management unit (MMU) [24].…”

Section: Related Workmentioning

confidence: 99%

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A component model of spatial locality

Gu¹,

Christopher

Bai

et al. 2009

Proceedings of the 2009 International Symposium on Memory Management

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Good spatial locality alleviates both the latency and bandwidth problem of memory by boosting the effect of prefetching and improving the utilization of cache. However, conventional definitions of spatial locality are inadequate for a programmer to precisely quantify the quality of a program, to identify causes of poor locality, and to estimate the potential by which spatial locality can be improved.This paper describes a new, component-based model for spatial locality. It is based on measuring the change of reuse distances as a function of the data-block size. It divides spatial locality into components at program and behavior levels. While the base model is costly because it requires the tracking of the locality of every memory access, the overhead can be reduced by using small inputs and by extending a sampling-based tool. The paper presents the result of the analysis for a large set of benchmarks, the cost of the analysis, and the experience of a user study, in which the analysis helped to locate a data-layout problem and improve performance by 7% with a 6-line change in an application with over 2,000 lines.

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