Dynamic analysis of Java program concepts for visualization and profiling

Singer, Jeremy; Kirkham, Chris

doi:10.1016/j.scico.2007.07.006

Cited by 9 publications

(7 citation statements)

References 28 publications

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“…Most of these views focus on particular aspects of program performance and are complementary to the view o ered by a feature-speci c pro ler. Some recent examples include Singer and Kirkham's (2008) pro ler, which assigns costs to programmer-annotated code regions, listener latency pro ling (Jovic and Hauswirth 2011), which reports high-latency operations, and Tamayo et al's (2012) tool, which provides information about the cost of database operations. One notable example, MAJOR (Binder et al 2011), uses Aspect Oriented Programming with inter-advice communication to create these complementary views.…”

Section: Related Workmentioning

confidence: 99%

Feature-Specific Profiling

Andersen¹,

St-Amour²,

Vítek³

et al. 2018

Preprint

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While high-level languages come with signi cant readability and maintainability bene ts, their performance remains di cult to predict. For example, programmers may unknowingly use language features inappropriately, which cause their programs to run slower than expected. To address this issue, we introduce feature-speci c pro ling, a technique that reports performance costs in terms of linguistic constructs. Festure-speci c pro lers help programmers nd expensive uses of speci c features of their language. We describe the architecture of a pro ler that implements our approach, explain prototypes of the pro ler for two languages with di erent characteristics and implementation strategies, and provide empirical evidence for the approach's general usefulness as a performance debugging tool.

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Section: Related Workmentioning

confidence: 99%

Feature-Specific Profiling

Andersen¹,

St-Amour²,

Vítek³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Shen et al [2004] detect locality phases using variable-distance sampling, wavelet filtering, and optimal phase partitioning. Singer and Kirkham [2008] investigate the possibility of using concept information within a framework for dynamic analysis of programs. They demonstrate two different styles of concept visualization, which show the proportion of overall time spent in each concept and the sequence of concept execution, respectively.…”

Section: Quality Of the Real-time Detectormentioning

confidence: 99%

“…Request permissions from permissions@acm.org. understanding Peleg and Mendelson [2007], Singer and Kirkham [2008], Pirzadeh et al [2011], Benomar et al [2014]. They are further interested in phases as a program runs in order to determine whether the program is operating as expected.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Program-Specific Phase Change Detection for Java Programs

Chiu

Marlin

Moss

2016

Proceedings of the 13th International Conference on Principles and Practices of Programming on the Java Platform: Virtual Machi

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It is well-known that programs tend to have multiple phases in their execution. Because phases have impact on micro-architectural features such as caches and branch predictors, they are relevant to program performance Xian et al. [2007], Roh et al. [2009], Gu and Verbrugge [2008] and energy consumption. They are also relevant to detecting whether a program is executing as expected or is encountering unusual or exceptional conditions, a software engineering and program monitoring concern Peleg and Mendelson [2007], Singer and Kirkham [2008], Pirzadeh et al. [2011], Benomar et al. [2014]. We offer here a method for real-time phase change detection in Java programs. After applying a training protocol to a program of interest, our method can detect phase changes at run time for that program with good precision and recall (compared with a "ground truth" definition of phases) and with small performance impact (average less than 2%). We also offer improved methodology for evaluating phase change detection mechanisms. In sum, our approach offers the first known implementation of real-time phase detection for Java programs. CCS Concepts •Computer systems organization → Real-time systems; •Computing methodologies → Machine learning; •Software and its engineering → Runtime environments;

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“…We briefly mention recent pieces of relevant work that provide alternative profiling views. Singer and Kirkham (2008) use the notion of conceptsprogrammer-annotated program portions that are responsible for a given task-to assign costs when profiling programs. Listener latency profiling (Jovic and Hauswirth 2011) helps programmers tune interactive applications by reporting high-latency operations, as opposed to operations with long execution times.…”

Section: Alternative Profiling Viewsmentioning

confidence: 99%

Feature-Specific Profiling

St-Amour

Andersen

Felleisen

2015

Lecture Notes in Computer Science

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High-level language and library features provide significant readability and maintainability benefits. Their performance costs, however, are usually not predictable, at least not easily. Programmers may accidentally misuse features and produce underperforming programs as a result.This paper introduces feature-specific profiling, a profiling technique that reports performance costs in terms of language and library features. With a feature-specific profiler, a programmer can identify specific feature instances that are responsible for performance problems. In addition the paper presents the architecture of our feature-specific profiler. At a high level, the profiler consists of two pieces: a generic kernel and feature-specific plug-ins. The API between the two relies on a lightweight form of stack inspection.

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Dynamic analysis of Java program concepts for visualization and profiling

Cited by 9 publications

References 28 publications

Feature-Specific Profiling

Feature-Specific Profiling

Real-Time Program-Specific Phase Change Detection for Java Programs

Feature-Specific Profiling

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