A Java vs. C++ Performance Evaluation: A 3D Modeling Benchmark

Gherardi, Luca; Brugali, Davide; Comotti, D.

doi:10.1007/978-3-642-34327-8_17

Cited by 18 publications

(10 citation statements)

References 7 publications

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“…al. [29] analysed the runtime performance of Java and C++ in the implementation of robotics algorithms. The study reported that on Windows platform, Java is around 1.09 to 1.51 times slower than C++ and around 1.21 to 1.91 times slower on Linux platform.…”

Section: Related Workmentioning

confidence: 99%

Comparison of C++ and Java in Implementing Introductory Programming Algorithms

Naveed

2021

QUEST Research Journal

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Computer programming is the heart of computer science and C++ and Java are the most popular and widely used languages. The performance of C++ and Java programming languages has been benchmarked using robotics, numerical, genetic and evolutionary algorithms, but not using introductory programming algorithms. This article examined C++ and Java by analysing the implementation of novice algorithms of introductory programming courses by evaluating the difficulty, effort, time and delivered bugs of programs with Halstead software metrics. The study identified that Java is more difficult than C++ in the implementation of selected algorithms. Similarly, the efforts required to translate the novice algorithms in Java are higher than C++. The time involved in translating selected algorithms in C++ is lower than Java. Similarly, the number of delivered bugs in C++ is lower than Java. The study suggests that C++ is more suitable than Java for the implementation of introductory programming algorithms and reasonably more suitable for introductory programming courses. The study emboldens the programming linguists to do further analyses in contemplating other programming languages with Halstead software metrics and other kinds of algorithms.

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Section: Related Workmentioning

confidence: 99%

Comparison of C++ and Java in Implementing Introductory Programming Algorithms

Naveed

2021

QUEST Research Journal

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“…However, we believe extensive comparisons between the performance of model transformation languages and general-purpose programming languages to be necessary to abolish the prejudice that dedicated transformation languages cannot outperform current compilers. Comparison of performance between different programming languages that are used for the same purpose is a wellestablished practice demonstrated by comparisons between Java and C++ for robotics programming done by Gherardi, Brugali and Comotti [27] or C++ and F90 for scientific programming by Cary et al [18]. Performance comparisons are also common practice in other domains such as GPU programming where specialized DSLs are used and performance is of high importance (Karimi et al [24]).…”

Section: Research Directionmentioning

confidence: 99%

Claimed advantages and disadvantages of (dedicated) model transformation languages: a systematic literature review

2020

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There exists a plethora of claims about the advantages and disadvantages of model transformation languages compared to general-purpose programming languages. With this work, we aim to create an overview over these claims in the literature and systematize evidence thereof. For this purpose, we conducted a systematic literature review by following a systematic process for searching and selecting relevant publications and extracting data. We selected a total of 58 publications, categorized claims about model transformation languages into 14 separate groups and conceived a representation to track claims and evidence through the literature. From our results, we conclude that: (i) the current literature claims many advantages of model transformation languages but also points towards certain deficits and (ii) there is insufficient evidence for claimed advantages and disadvantages and (iii) there is a lack of research interest into the verification of claims.

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“…Most existing platforms use Java/Groovy as the main language. Despite boasting good multi-platform support and being a modern object-oriented language, the performance of Java compared to C and C++ is debated [11], [12]. It is possible to deploy TensorFlow-based models in Java, with libraries like DeepLearning4J [13], but its support for layers and network architectures is currently limited.…”

Section: Introductionmentioning

confidence: 99%

FastPathology: An Open-Source Platform for Deep Learning-Based Research and Decision Support in Digital Pathology

et al. 2021

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Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs) requires advanced memory handling to read, display and process these images. There are several open-source platforms for working with WSIs, but few support deployment of CNN models. These applications use thirdparty solutions for inference, making them less user-friendly and unsuitable for high-performance image analysis. To make deployment of CNNs user-friendly and feasible on low-end machines, we have developed a new platform, FastPathology, using the FAST framework and C++. It minimizes memory usage for reading and processing WSIs, deployment of CNN models, and real-time interactive visualization of results. Runtime experiments were conducted on four different use cases, using different architectures, inference engines, hardware configurations and operating systems. Memory usage for reading, visualizing, zooming and panning a WSI were measured, using FastPathology and three existing platforms. FastPathology performed similarly in terms of memory to the other C++-based application, while using considerably less than the two Java-based platforms. The choice of neural network model, inference engine, hardware and processors influenced runtime considerably. Thus, FastPathology includes all steps needed for efficient visualization and processing of WSIs in a single application, including inference of CNNs with real-time display of the results. Source code, binary releases, video demonstrations and test data can be found online

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A Java vs. C++ Performance Evaluation: A 3D Modeling Benchmark

Cited by 18 publications

References 7 publications

Comparison of C++ and Java in Implementing Introductory Programming Algorithms

Comparison of C++ and Java in Implementing Introductory Programming Algorithms

Claimed advantages and disadvantages of (dedicated) model transformation languages: a systematic literature review

FastPathology: An Open-Source Platform for Deep Learning-Based Research and Decision Support in Digital Pathology

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