Argument Controlled Profiling

Küstner, Tilman; Weidendorfer, Josef; Weinzierl, Tobias

doi:10.1007/978-3-642-14122-5_22

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Since the RMS is a measure of distinct accessed memory cells, it also fails to characterize computations whose running time is determined by the value of some variable: e.g., the RMS of the naive algorithm for computing the factorial of a number n would be constant, regardless of the value of n. Aggregating performance measurements by distinct values of function arguments is an alternative approach that is explored, e.g., in [37].…”

Section: Construction Validitymentioning

confidence: 99%

Input-Sensitive Profiling

Coppa

Demetrescu

Finocchi

2014

IIEEE Trans. Software Eng.

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In this article we present a building block technique and a toolkit towards automatic discovery of workload-dependent performance bottlenecks. From one or more runs of a program, our profiler automatically measures how the performance of individual routines scales as a function of the input size, yielding clues to their growth rate. The output of the profiler is, for each executed routine of the program, a set of tuples that aggregate performance costs by input size. The collected profiles can be used to produce performance plots and derive trend functions by statistical curve fitting techniques. A key feature of our method is the ability to automatically measure the size of the input given to a generic code fragment: to this aim, we propose an effective metric for estimating the input size of a routine and show how to compute it efficiently. We discuss several examples, showing that our approach can reveal asymptotic bottlenecks that other profilers may fail to detect and can provide useful characterizations of the workload and behavior of individual routines in the context of mainstream applications, yielding several code optimizations as well as algorithmic improvements. To prove the feasibility of our techniques, we implemented a Valgrind tool called aprof and performed an extensive experimental evaluation on the SPEC CPU2006 benchmarks. Our experiments show that aprof delivers comparable performance to other prominent Valgrind tools, and can generate informative plots even from single runs on typical workloads for most algorithmically-critical routines

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Section: Construction Validitymentioning

confidence: 99%

Input-Sensitive Profiling

Coppa

Demetrescu

Finocchi

2014

IIEEE Trans. Software Eng.

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“…Hence, it fails to characterize computations whose running time is determined by the value of some variable: e.g., the RMS of the naive algorithm for computing the factorial of a number n would be constant, regardless of the value of n. Aggregating performance measurements by distinct values of function arguments is an alternative approach that is explored, e.g., in [28].…”

Section: Characterizing Asymptotic Behaviormentioning

confidence: 99%

Input-sensitive profiling

Coppa

Demetrescu

Finocchi

2012

Proceedings of the 33rd ACM SIGPLAN Conference on Programming Language Design and Implementation

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In this paper we present a profiling methodology and toolkit for helping developers discover hidden asymptotic inefficiencies in the code. From one or more runs of a program, our profiler automatically measures how the performance of individual routines scales as a function of the input size, yielding clues to their growth rate. The output of the profiler is, for each executed routine of the program, a set of tuples that aggregate performance costs by input size. The collected profiles can be used to produce performance plots and derive trend functions by statistical curve fitting or bounding techniques. A key feature of our method is the ability to automatically measure the size of the input given to a generic code fragment: to this aim, we propose an effective metric for estimating the input size of a routine and show how to compute it efficiently. We discuss several case studies, showing that our approach can reveal asymptotic bottlenecks that other profilers may fail to detect and characterize the workload and behavior of individual routines in the context of real applications. To prove the feasibility of our techniques, we implemented a Valgrind tool called aprof and performed an extensive experimental evaluation on the SPEC CPU2006 benchmarks. Our experiments show that aprof delivers comparable performance to other prominent Valgrind tools, and can generate informative plots even from single runs on typical workloads for most algorithmically-critical routines

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“…To address this problem, input-sensitive profiling was introduced where the sizes of their inputs and the values of the input parameters are varied to uncover performance problems in the AUT [86,20,51]. Essentially, the standard profiling procedure that we described above is extended with three more steps: 3) study the code of the AUT to understand which methods are specific to certain classes of inputs; 4) construct different combinations of input values to the AUT to find methods involved in bottlenecks, and 5) analyze different execution traces for different combinations of input values to generalize the results.…”

Section: Introductionmentioning

confidence: 99%

Automating performance bottleneck detection using search-based application profiling

Shen

Luo

Poshyvanyk

et al. 2015

Proceedings of the 2015 International Symposium on Software Testing and Analysis

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Application profiling is an important performance analysis technique, when an application under test is analyzed dynamically to determine its space and time complexities and the usage of its instructions. A big and important challenge is to profile nontrivial web applications with large numbers of combinations of their input parameter values. Identifying and understanding particular subsets of inputs leading to performance bottlenecks is mostly manual, intellectually intensive and laborious procedure.We propose a novel approach for automating performance bottleneck detection using search-based input-sensitive application profiling. Our key idea is to use a genetic algorithm as a search heuristic for obtaining combinations of input parameter values that maximizes a fitness function that represents the elapsed execution time of the application. We implemented our approach, coined as Genetic Algorithm-driven Profiler (GA-Prof) that combines a search-based heuristic with contrast data mining of execution traces to accurately determine performance bottlenecks. We evaluated GA-Prof to determine how effectively and efficiently it can detect injected performance bottlenecks into three popular open source web applications. Our results demonstrate that GA-Prof efficiently explores a large space of input value combinations while automatically and accurately detecting performance bottlenecks, thus suggesting that it is effective for automatic profiling.

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Argument Controlled Profiling

Cited by 4 publications

References 10 publications

Input-Sensitive Profiling

Input-Sensitive Profiling

Input-sensitive profiling

Automating performance bottleneck detection using search-based application profiling

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