This article presents the results of a literature study related to the construction and operation of Java Virtual Machine, as well as performance tests of selected languages using the aforementioned ...runtime environment on the example of Java, Scala and Kotlin. Performance testing was carried out using two applications built using the Apache Maven archetype with the built-in Java Microbenchmark Harness library.
This paper discusses the issue of comparing Java and Kotlin technologies based on the web application framework. The criteria taken into account for testing purposes are: execution time, memory ...usage, CPU load, database response in set time. A series of tests and their in-depth comparative analysis are carried out. For this case, tests and code analysis were carried out to draw comparative conclusions. The performance in terms of web frameworks, database response speed and tests implementation in different languages - in all these Kotlin proved to be less efficient. There is no significant difference between CPU load between individual easurements, the difference does not exceed 2%. Implementation in the Kotlin language has never achieved the best result in any group of measurements.
The article presents a comparison of Kotlin coroutines with analogous solutions in Java and Scala in parallel programming using chosen metric and non-metric criteria. For that purpose, a multi-module ...project with corresponding implementations of selected algorithms in all of the three languages was created and then analyzed. The studies were preceded by a description of the created project.
Vincent: Green hot methods in the JVM Liu, Kenan; Mahmoud, Khaled; Yoo, Joonhwan ...
Science of computer programming,
August 2023, 2023-08-00, Volume:
230
Journal Article
Peer reviewed
In this paper, we show the energy efficiency of Java applications can be improved by applying Dynamic Voltage and Frequency Scaling (DVFS) inside the Java Virtual Machine (JVM). We augment the JVM to ...record the energy consumption of hot methods as the underlying CPU is run at different clock frequencies; after all the frequency possibilities for a method have been explored, the execution of the method in an optimized run is set to the CPU frequency that leads to the most energy-efficient execution for that method. We introduce a new sampling methodology to overcome the dual challenges in our design: both the underlying measurement mechanism for energy profiling and the DVFS for energy optimization are overhead-prone. We extend JikesRVM with our approach and benchmark it over the DaCapo suite on a server-class Linux machine. Experiments show we are able to use 14.9% less energy than built-in power management in Linux, and improve energy efficiency by 21.1% w.r.t. the metric of Energy-Delay Product (EDP).
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Java Virtual Machine (JVM) is the fundamental software system that supports the interpretation and execution of Java bytecode. To support the surging performance demands for the increasingly complex ...and large-scale Java programs, JustIn-Time (JIT) compiler was proposed to perform sophisticated runtime optimization. However, this inevitably induces various bugs, which are becoming more pervasive over the decades and can often cause significant consequences. To facilitate the design of effective and efficient testing techniques to detect JIT compiler bugs. This study first performs a preliminary study aiming to understand the characteristics of JIT compiler bugs and the corresponding triggering test cases. Inspired by the empirical findings, we propose JOpFuzzer, a new JVM testing approach with a specific focus on JIT compiler bugs. The main novelty of JOpFuzzer is embodied in three aspects. First, besides generating new seeds, JOpFuzzer also searches for diverse configurations along the new dimension of optimization options. Second, JOpFuzzer learns the correlations between various code features and different optimization options to guide the process of seed mutation and option exploration. Third, it leverages the profile data, which can reveal the program execution information, to guide the fuzzing process. Such novelties enable JOpFuzzer to effectively and efficiently explore the two-dimensional input spaces. Extensive evaluation shows that JOpFuzzer outperforms the state-of-the-art approaches in terms of the achieved code coverages. More importantly, it has detected 41 bugs in OpenJDK, and 25 of them have already been confirmed or fixed by the corresponding developers.
•Proposing a multi-level analysis tool for Java applications.•Using of a low-overhead tracer to collect Kernel and userspace traces.•Creating a comprehensive visualization system.
Performance ...analysis of Java applications requires a deep understanding of the Java virtual machine and the system on which it is running. An unexpected latency can be caused by a bug in the source code, a misconfiguration or an external factor like CPU or disk contention. Existing tools have difficulties finding the root cause of some latencies because they do not efficiently collect performance data from the different layers of the system. In this paper, we propose a multilevel analysis framework that uses Kernel and userspace tracing to help developers understand and evaluate the performance of their applications. Kernel tracing is used to gather information about thread scheduling, system calls, I/O operations, etc. and userspace tracing is used to monitor the internal components of the JVM such as the garbage collectors and the JIT compilers. By bridging the gap between kernel and userspace traces, our tool provides full visibility to developers and helps them diagnose difficult performance issues. We show the usefulness of our approach by using it to detect problems in different Java applications.
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Intel SGX enables developers to protect security critical parts of their application code and data even from privileged software. This type of protection is needed in all cases where applications run ...on untrusted infrastructures, including public clouds. Since a significant fraction of current applications is written in Java, the research strand on how to fully unleash the potential of SGX in Java is flourishing, and multiple techniques have been proposed. In this paper, we review such techniques, and select the most promising ones – namely SCONE, SGX-LKL, and SGX-JNI Bridge – for an experimental comparison with respect to effort, security, and performance. We use a benchmark application from a real-world case study based on microservices – possibly the most prominent software architecture for current applications – and built on the widely adopted Vert.x development framework. We focus on specific microservices characterized by three different profiles in terms of resource usage – I/O-, CPU-, and Memory-intensive – and assess the trade-offs of the three aforementioned techniques for SGX integration. The results of the analysis can be used as a reference by practitioners willing to identify the best approach for integrating SGX in their Java applications, based on priorities of their particular context.
•This paper overviews current approaches for securing Java software with Intel SGX.•It compares three techniques with respect to effort, security, and performance.•It provides an evaluation using I/O, CPU, and Memory intensive cloud microservices.
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Call graphs have many applications in software engineering, including bug-finding, security analysis, and code navigation in IDEs. However, the construction of call graphs requires significant ...investment in program analysis infrastructure. An increasing number of programming languages compile to the Java Virtual Machine (JVM), and program analysis frameworks such as WALA and SOOT support a broad range of program analysis algorithms by analyzing JVM bytecode. This approach has been shown to work well when applied to bytecode produced from Java code. In this paper, we show that it also works well for diverse other JVM-hosted languages: dynamically-typed functional Scheme, statically-typed object-oriented Scala, and polymorphic functional OCaml. Effectively, we get call graph construction for these languages for free, using existing analysis infrastructure for Java, with only minor challenges to soundness. This, in turn, suggests that bytecode-based analysis could serve as an implementation vehicle for bug-finding, security analysis, and IDE features for these languages. We present qualitative and quantitative analyses of the soundness and precision of call graphs constructed from JVM bytecodes for these languages, and also for Groovy, Clojure, Python, and Ruby. However, we also show that implementation details matter greatly. In particular, the JVM-hosted implementations of Groovy, Clojure, Python, and Ruby produce very unsound call graphs, due to the pervasive use of reflection, invokedynamic instructions, and run-time code generation. Interestingly, the dynamic translation schemes employed by these languages, which result in unsound static call graphs, tend to be correlated with poor performance at run time.
This paper describes the Jas4pp framework for exploring physics cases and for detector-performance studies of future particle collision experiments. Jas4pp is a multi-platform Java program for ...numeric calculations, scientific visualization in 2D and 3D, storing data in various file formats and displaying collision events and detector geometries. It also includes complex data-analysis algorithms for function minimization, regression analysis, event reconstruction (such as jet reconstruction), limit settings and other libraries widely used in particle physics. The framework can be used with several scripting languages, such as Python/Jython, Groovy and JShell. Several benchmark tests discussed in the paper illustrate significant improvements in the performance of the Groovy and JShell scripting languages compared to the standard Python implementation in C. The improvements for numeric computations in Java are attributed to recent enhancements in the Java Virtual Machine.
Program title: Jas4pp
CPC Library link to program files:https://doi.org/10.17632/jzvddk26cy.1
Developer’s repository link:https://atlaswww.hep.anl.gov/asc/jas4pp/
Licensing provisions: GNU General Public License 3
Programming language: Java, Jython, Groovy
Nature of problem: Develop a platform-independent data-analysis framework for high-energy and nuclear physics (HEP and NP) with a support of fast dynamically-typed scripting languages, comprehensive data-visualisation and I/O libraries.
Solution method: The solution adopted here is to use Java and the scripting languages integrated with Java VM.
Additional comments: All 3rd party Java libraries included with this program are licensed by GPLv3, GNU Lesser General Public License (LGPL) or by other licenses compatible with the GPLv3 license, and adhere to Mendeley Data approved open-source software licenses. These licenses files are includes with the program.
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