Software probes: towards a quick method for machine characterization and application performance prediction

Strube, Alexandre; Rexachs, Dolores; Luque, Emilio

doi:10.1109/ispdc.2008.40

Cited by 9 publications

(8 citation statements)

References 24 publications

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“…A hierarchical view of how much time is spent in a given loop and the loops nested within it is provided. Stube et al [55] provide Pin-based software probes for machine characterization and application performance prediction. Patil et al [46] present a Pin-based framework-PinPlay-for deterministic replay and reproducible analysis of parallel programs.…”

Section: Pin-based Toolsmentioning

confidence: 99%

Trin-Trin: Who’s Calling? A Pin-Based Dynamic Call Graph Extraction Framework

Jalan

Kejariwal

2012

Int J Parallel Prog

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Multi-core based systems are ubiquitous in data centers. Efficient exploitation of hardware parallelism supported by such systems is imperative on multiple fronts: minimizing latency and power consumption and maximizing throughput. This in turn calls for advanced program analysis and optimization. Call graphs have been long used to this end. Although several static call graph extraction techniques have been proposed in the past, these techniques cannot be applied to analyze programs already running in production. Likewise, the existing dynamic call graph extraction tools have limited use in production owing to, say (but not limited to), lack of support for capturing wall clock time spent in functions of a given program and lack of means to analyze the call graph information captured at run time. In this paper, we present a Pin-based dynamic call graph extraction framework called Trin-Trin. The framework enables extraction of complete, precise and dynamic call graphs. Additionally, the framework can be used seamlessly with already running applications. Furthermore, an analytics engine is provided to facilitate advanced program analysis, e.g., different multithreading context(s) of any function can be extracted in a demand-driven fashion. We evaluate the overhead of Trin-Trin using several Unix utilities, applications from the industry-standard SPEC CINT2006, CFP2006 benchmark suite and Yahoo! properties. Additionally, we present a case study to illustrate how Trin-Trin can be used to analyze performance bottlenecks and performance regressions.

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Section: Pin-based Toolsmentioning

confidence: 99%

Trin-Trin: Who’s Calling? A Pin-Based Dynamic Call Graph Extraction Framework

Jalan

Kejariwal

2012

Int J Parallel Prog

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“…The simulator takes the application CDFG and HW graph as the input and carries the simulation at a low level to catch the detailed HW activities. Source code analysis is also performed in the case of [10]. Analyzing the source code in order to conclude to resource requirements is probably the optimal solution.…”

Section: Ii-1: Discrete Roles In the Cloud Stackmentioning

confidence: 99%

Translation of application-level terms to resource-level attributes across the Cloud stack layers

Kousiouris

Kyriazis

Gogouvitis

et al. 2011

2011 IEEE Symposium on Computers and Communications (ISCC)

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Abstract-The emergence of new environments such as Cloud computing highlighted new challenges in traditional fields like performance estimation. Most of the current cloud environments follow the Software, Platform, Infrastructure service model in order to map discrete roles / providers according to the offering in each "layer". However, the limited amount of information passed from one layer to the other has raised the level of difficulty in translating user-understandable application terms from the Software layer to resource specific attributes, which can be used to manage resources in the Platform and Infrastructure layers. In this paper, a generic black box approach, based on Artificial Neural Networks is used in order to perform the aforementioned translation. The efficiency of the approach is presented and validated through different application scenarios (namely FFMPEG encoding and real-time interactive eLearning) that highlight its applicability even in cases where accurate performance estimation is critical, as in cloud environments aiming to facilitate real-time and interactivity.

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“…For instance rules working on the attribute submission-time may trigger the creation of clusters containing jobs filtered by timeframes of e.g. 8 hours (0-8, 8-16, 16-24) or 12 hours (8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20). Each rule and consequently the respective clusters are specified by a unique id that serves for their (de)activation during the prediction process.…”

Section: Rules and Clustersmentioning

confidence: 99%

Adaps – A three-phase adaptive prediction system for the run-time of jobs based on user behaviour

Glasner

Volkert

2011

Journal of Computer and System Sciences

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In heterogeneous and distributed environments it is necessary to create schedules for utilising resources in an efficient way. This generation often poses a problem for a scheduler, since several aspects have to be considered. One way of supporting a scheduler is to provide accurate predictions of the run-times of the submitted jobs. A large number of current techniques offer statistical models that are deployed on previously filtered data. As users have different jobs, and because the attributes of their jobs differ, filtering data and choosing an appropriate prediction method has to cover these aspects. This article describes Adaps, a system for run-time prediction that works in three phases. Each is independently adjusting to the jobs of a user, based on historical information. This leads to a user specific clustering of data and to a flexible utilisation of different prediction techniques in order to create a user-centred prediction model.

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Software probes: towards a quick method for machine characterization and application performance prediction

Cited by 9 publications

References 24 publications

Trin-Trin: Who’s Calling? A Pin-Based Dynamic Call Graph Extraction Framework

Trin-Trin: Who’s Calling? A Pin-Based Dynamic Call Graph Extraction Framework

Translation of application-level terms to resource-level attributes across the Cloud stack layers

Adaps – A three-phase adaptive prediction system for the run-time of jobs based on user behaviour

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