A framework for reducing the cost of instrumented code

Arnold, Matthew; Ryder, Barbara G.

doi:10.1145/378795.378832

Cited by 257 publications

(158 citation statements)

References 41 publications

(50 reference statements)

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“…We use an overlap percentage metric as in references [2,26] to compare the profiles collected on the two platforms. Informally, the overlap is the amount of profiled information (weighted by execution frequency) that is present in both profiles.…”

Section: Reproducibility Of Profilesmentioning

confidence: 99%

Platform‐independent profiling in a virtual execution environment

et al. 2008

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SUMMARYVirtual execution environments, such as the Java Virtual Machine, promote platform-independent software development. However, when it comes to analyzing algorithm complexity and performance bottlenecks, available tools focus on platform-specific metrics, such as e.g. the CPU time consumption on a particular system. Other drawbacks of many prevailing profiling tools are high overhead, significant measurement perturbation, as well as reduced portability of profiling tools, which are often implemented in platformdependent native code. This article presents a novel profiling approach, which is entirely based on program transformation techniques, in order to build a profiling data structure that provides calling-contextsensitive program execution statistics. We explore the use of platform-independent profiling metrics, in order to make the instrumentation entirely portable and to generate reproducible profiles. We implemented these ideas within a Java-based profiling tool called JP. A significant novelty is that this tool achieves complete bytecode coverage, by statically instrumenting the core runtime libraries, and dynamically instrumenting the rest of the code. JP provides a small and flexible API to write customized profiling agents in pure Java, which are periodically activated to process the collected profiling information. Performance measurements point out that, despite the presence of dynamic instrumentation, JP causes significantly less overhead than a prevailing tool for the profiling of Java code.

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Section: Reproducibility Of Profilesmentioning

confidence: 99%

Platform‐independent profiling in a virtual execution environment

et al. 2008

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“…Once the skipping process is part of the JVM, its overhead could be lowered. For a discussion of possible optimizations when incorporating profiling into a JVM, see [2]. Once the OSP algorithm is fully integrated into a JVM, its output could be used to locate possible candidates for JIT compilation.…”

Section: Discussionmentioning

confidence: 99%

“…In [2], Arnold and Ryder proposed to maintain two versions of the program in memory -one instrumented, and one almost uninstrumented. The program execution can then jump between these two versions, collecting enough data for effective profiling, but keeping the overhead low.…”

Section: Related Workmentioning

confidence: 99%

Online Subpath Profiling

Oren

Matias

Sagiv

2002

Lecture Notes in Computer Science

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Abstract. We present an efficient online subpath profiling algorithm, OSP, that reports hot subpaths executed by a program in a given run. The hot subpaths can start at arbitrary basic block boundaries, and their identification is important for code optimization; e.g., to locate program traces in which optimizations could be most fruitful, and to help programmers in identifying performance bottlenecks. The OSP algorithm is online in the sense that it reports at any point during execution the hot subpaths as observed so far. It has very low memory and runtime overheads, and exhibits high accuracy in reports for benchmarks such as JLex and FFT. These features make the OSP algorithm potentially attractive for use in just-in-time (JIT) optimizing compilers, in which profiling performance is crucial and it is useful to locate hot subpaths as early as possible. The OSP algorithm is based on an adaptive sampling technique that makes effective utilization of memory with small overhead. Both memory and runtime overheads can be controlled, and the OSP algorithm can therefore be used for arbitrarily large applications, realizing a tradeoff between report accuracy and performance. We have implemented a Java prototype of the OSP algorithm for Java programs. The implementation was tested on programs from the Java Grande benchmark suite and exhibited a low average runtime overhead.

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“…Note that our implementation has not been aggressively optimized. It has been shown [1] that combining instrumentation and sampling profiling leaded to accurate profiles (93-98% overlap with a perfect profile) with low overhead (3-6%). The profilers we presented in this paper are likely to benefit from such instrumentation sampling.…”

Section: Micro-benchmarkmentioning

confidence: 99%

Domain-Specific Profiling

Bergel

Nierstrasz

Renggli

et al. 2011

Objects, Models, Components, Patterns

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Abstract. Domain-specific languages and models are increasingly used within general-purpose host languages. While traditional profiling tools perform well on host language code itself, they often fail to provide meaningful results if the developers start to build and use abstractions on top of the host language. In this paper we motivate the need for dedicated profiling tools with three different case studies. Furthermore, we present an infrastructure that enables developers to quickly prototype new profilers for their domain-specific languages and models.

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A framework for reducing the cost of instrumented code

Cited by 257 publications

References 41 publications

Platform‐independent profiling in a virtual execution environment

Platform‐independent profiling in a virtual execution environment

Online Subpath Profiling

Domain-Specific Profiling

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