A Comprehensive Study on the Energy Efficiency of Java’s Thread-Safe Collections

Pinto, Gustavo; Kenan, Liu; Castor, Fernando; Liu, Yu David

doi:10.1109/icsme.2016.34

Cited by 29 publications

(26 citation statements)

References 40 publications

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“…Following the approach considered in different studies [37], [38], [39], our benchmark is inspired by the microbenchmark to evaluate the run time performance of Java's JDK Collection API implementations [40]. The operations we consider are listed in Table II, and they all can be abstracted by the format:…”

Section: B Benchmarkmentioning

confidence: 99%

Haskell in Green Land: Analyzing the Energy Behavior of a Purely Functional Language

Lima

Soares-Neto

Lieuthier

et al. 2016

2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

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Recent work has studied the effect that factors such as code obfuscation, refactorings and data types have on energy efficiency. In this paper, we attempt to shed light on the energy behavior of programs written in a lazy purely functional language, Haskell. We have conducted two empirical studies to analyze the energy efficiency of Haskell programs from two different perspectives: strictness and concurrency. Our experimental space exploration comprises more than 2000 configurations and 20000 executions.We found out that small changes can make a big difference in terms of energy consumption. For example, in one of our benchmarks, under a specific configuration, choosing one data sharing primitive (MVar) over another (TMVar) can yield 60% energy savings. In another benchmark, the latter primitive can yield up to 30% energy savings over the former. Thus, tools that support developers in quickly refactoring a program to switch between different primitives can be of great help if energy is a concern. In addition, the relationship between energy consumption and performance is not always clear. In sequential benchmarks, high performance is an accurate proxy for low energy consumption. However, for one of our concurrent benchmarks, the variants with the best performance also exhibited the worst energy consumption. To support developers in better understanding this complex relationship, we have extended two existing performance analysis tools to also collect and present data about energy consumption.

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Section: B Benchmarkmentioning

confidence: 99%

Haskell in Green Land: Analyzing the Energy Behavior of a Purely Functional Language

Lima

Soares-Neto

Lieuthier

et al. 2016

2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

Self Cite

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“…Studies have shown how different design patterns [62,63], sorting algorithms [64,65], android API and advertisements [6,7,66,67], software version changes [10], code obfuscations [68], refactorings and transformations [9,69,70], and different Java based collections [29,71,72,73] have a statistically significant impact on energy usage. Studies have also shown how different programming languages have very different energy usages [24,30,74].…”

Section: Related Workmentioning

confidence: 99%

SPELLing out energy leaks: Aiding developers locate energy inefficient code

Pereira

Carção

Couto

et al. 2020

Journal of Systems and Software

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Although hardware is generally seen as the main culprit for a computer's energy usage, software too has a tremendous impact on the energy spent. Unfortunately, there is still not enough support for software developers so they can make their code more energy-aware.This paper proposes a technique to detect energy inefficient fragments in the source code of a software system. Test cases are executed to obtain energy consumption measurements, and a statistical method, based on spectrumbased fault localization, is introduced to relate energy consumption to the source code. The result of our technique is an energy ranking of source code fragments pointing developers to possible energy leaks in their code. This technique was implemented in the SPELL toolkit.Finally, in order to evaluate our technique, we conducted an empirical study where we asked participants to optimize the energy efficiency of a software system using our tool, while also having two other groups using no tool assistance and a profiler, respectively. We showed statistical evidence that developers using our technique were able to improve the energy efficiency by 43% on average, and even out performing a profiler for energy optimization.

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“…This approach is particularly common in empirical studies, where the energy consumption of specific program features is reported based on instrumenting such features. With a feature focus Ð e.g., the use of the concurrent collections API [25,45], or the impact of data access patterns [36] Ð instrumentation can be a feasible solution as it can be placed for one code block at a time. In this use scenario, Chappie may be useful in improving the precision of profiling by making the profiler concurrency-aware.…”

Section: Related Workmentioning

confidence: 99%