26 research outputs found

    A scalable technique for characterizing the usage of temporaries in framework-intensive Java applications

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    Framework-intensive applications (e.g., Web applications) heavily use temporary data structures, often resulting in performance bot-tlenecks. This paper presents an optimized blended escape analysis to approximate object lifetimes and thus, to identify these tempo-raries and their uses. Empirical results show that this optimized analysis on average prunes 37 % of the basic blocks in our bench-marks, and achieves a speedup of up to 29 times compared to the original analysis. Newly defined metrics quantify key properties of temporary data structures and their uses. A detailed empirical eval-uation offers the first characterization of temporaries in framework-intensive applications. The results show that temporary data struc-tures can include up to 12 distinct object types and can traverse through as many as 14 method invocations before being captured

    08161 Abstracts Collection -- Scalable Program Analysis

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    From April 13 to April 18, 2008, the Dagstuhl Seminar 08161 ``Scalable Program Analysis\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

    The Requirements Editor RED

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    Visualizing the Results of a Complex Hybrid Dynamic-Static Analysis

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    Complex static or hybrid static-dynamic analyses produce large quantities of structured data. In the past, this data was generally intended for use by compilers or other software tools that used the produced information to transform the application being analyzed. However, it is becomingly increasingly common for the results of these analyses to be used directly by humans. For example, in our own prior work we have developed a hybrid dynamic-static escape analysis intended to help developers identify sources of object churn within large framework-base applications. In order to facilitate human use of complex analysis results, visualizations need to be developed that allow a user to browse these results and to identify the points of interest within these large data sets. In this paper we present Hi-C, a visualization tool for our hybrid escape analysis that has been implemented as an Eclipse plugin. We show how Hi-C can help developers identify sources of object churn in a large framework-based application and how we have used the tool to assist in understanding the results of a complex analysis

    Darwinian Data Structure Selection

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    Data structure selection and tuning is laborious but can vastly improve an application's performance and memory footprint. Some data structures share a common interface and enjoy multiple implementations. We call them Darwinian Data Structures (DDS), since we can subject their implementations to survival of the fittest. We introduce ARTEMIS a multi-objective, cloud-based search-based optimisation framework that automatically finds optimal, tuned DDS modulo a test suite, then changes an application to use that DDS. ARTEMIS achieves substantial performance improvements for \emph{every} project in 55 Java projects from DaCapo benchmark, 88 popular projects and 3030 uniformly sampled projects from GitHub. For execution time, CPU usage, and memory consumption, ARTEMIS finds at least one solution that improves \emph{all} measures for 86%86\% (37/4337/43) of the projects. The median improvement across the best solutions is 4.8%4.8\%, 10.1%10.1\%, 5.1%5.1\% for runtime, memory and CPU usage. These aggregate results understate ARTEMIS's potential impact. Some of the benchmarks it improves are libraries or utility functions. Two examples are gson, a ubiquitous Java serialization framework, and xalan, Apache's XML transformation tool. ARTEMIS improves gson by 16.516.5\%, 1%1\% and 2.2%2.2\% for memory, runtime, and CPU; ARTEMIS improves xalan's memory consumption by 23.523.5\%. \emph{Every} client of these projects will benefit from these performance improvements.Comment: 11 page

    Proceedings of the joint track "Tools", "Demos", and "Posters" of ECOOP, ECSA, and ECMFA, 2013

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    A Practical Blended Analysis for Dynamic Features in JavaScript

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    The JavaScript Blended Analysis Framework is designed to perform a general-purpose, practical combined static/dynamic analysis of JavaScript programs, while handling dynamic features such as run-time generated code and variadic func- tions. The idea of blended analysis is to focus static anal- ysis on a dynamic calling structure collected at runtime in a lightweight manner, and to rene the static analysis us- ing additional dynamic information. We perform blended points-to analysis of JavaScript with our framework and compare results with those computed by a pure static points- to analysis. Using JavaScript codes from actual webpages as benchmarks, we show that optimized blended analysis for JavaScript obtains good coverage (86.6% on average per website) of the pure static analysis solution and nds ad- ditional points-to pairs (7.0% on average per website) con- tributed by dynamically generated/loaded code

    Workload characterization of JVM languages

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    Being developed with a single language in mind, namely Java, the Java Virtual Machine (JVM) nowadays is targeted by numerous programming languages. Automatic memory management, Just-In-Time (JIT) compilation, and adaptive optimizations provided by the JVM make it an attractive target for different language implementations. Even though being targeted by so many languages, the JVM has been tuned with respect to characteristics of Java programs only -- different heuristics for the garbage collector or compiler optimizations are focused more on Java programs. In this dissertation, we aim at contributing to the understanding of the workloads imposed on the JVM by both dynamically-typed and statically-typed JVM languages. We introduce a new set of dynamic metrics and an easy-to-use toolchain for collecting the latter. We apply our toolchain to applications written in six JVM languages -- Java, Scala, Clojure, Jython, JRuby, and JavaScript. We identify differences and commonalities between the examined languages and discuss their implications. Moreover, we have a close look at one of the most efficient compiler optimizations - method inlining. We present the decision tree of the HotSpot JVM's JIT compiler and analyze how well the JVM performs in inlining the workloads written in different JVM languages
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