Towards a Green Ranking for Programming Languages

ABSTRACT

While in the past the primary goal to optimize software was the run time optimization, nowadays there is a growing awareness of the need to reduce energy consumption. Additionally, a growing number of developers wish to become more energy-aware when programming and feel a lack of tools and the knowledge to do so.

In this paper we define a ranking of energy efficiency in programming languages. We consider a set of computing problems implemented in ten well-known programming languages, and monitored the energy consumed when executing each language. Our preliminary results show that although the fastest languages tend to be the lowest consuming ones, there are other interesting cases where slower languages are more energy efficient than faster ones.

References

1. Sarah Abdulsalam, Ziliang Zong, Qijun Gu, and Meikang Qiu. 2015. Using the Greenup, Powerup, and Speedup metrics to evaluate software energy efficiency. In Proc. of the 6th Int. Green and Sustainable Computing Conf. IEEE, 1--8.
2. Tarsila Bessa, Pedro Quintão, Michael Frank, and Fernando Magno Quintão Pereira. 2016. JetsonLeap: A Framework to Measure Energy-Aware Code Optimizations in Embedded and Heterogeneous Systems. In Proc. of the 20th Brazilian Symposium on Programming Languages, Fernando Castor and Yu David Liu (Eds.). Springer Int. Publishing, 16--30.
3. Wei-Ngan Chin, Huu Hai Nguyen, Shengchao Qin, and Martin Rinard. 2005. Memory Usage Verification for OO Programs. In Static Analysis: 12th Int. Symposium, SAS 2005, London, UK, September 7--9, 2005. Proceedings, Chris Hankin and Igor Siveroni (Eds.). Springer Berlin Heidelberg, 70--86.
4. Thomas D Cook and Donald T Campbell. 1979. Quasi-experimentation: design & analysis issues for field settings. Houghton Mifflin.
5. Marco Couto, Tiago Carção, Jácome Cunha, João Paulo Fernandes, and João Saraiva. 2014. Detecting Anomalous Energy Consumption in Android Applications. In Programming Languages: 18th Brazilian Symposium, SBLP 2014, Maceio, Brazil, October 2--3, 2014. Proceedings, Fernando Magno Quintão Pereira (Ed.). Springer Int. Publishing, 77--91.
6. Martin Dimitrov, Carl Strickland, Seung-Woo Kim, Karthik Kumar, and Kshitij Doshi. 2015. Intel® Power Governor. https://software.intel.com/en-us/articles/intel-power-governor. (2015). Accessed: 2015-10-12.
7. Neville Grech, Kyriakos Georgiou, James Pallister, Steve Kerrison, Jeremy Morse, and Kerstin Eder. 2015. Static Analysis of Energy Consumption for LLVM IR Programs. In Proc. of the 18th Int. Workshop on Software and Compilers for Embedded Systems (SCOPES '15). ACM, 12--21.
8. Marcus Hähnel, Björn Döbel, Marcus Völp, and Hermann Härtig. 2012. Measuring energy consumption for short code paths using RAPL. SIGMETRICS Performance Evaluation Review 40, 3 (2012), 13--17.
9. Shuai Hao, Ding Li, William G. J. Halfond, and Ramesh Govindan. 2013. Estimating Mobile Application Energy Consumption Using Program Analysis. In Proc. of the 2013 Int. Conf. on Software Engineering (ICSE '13). IEEE Press, 92--101.
10. Samir Hasan, Zachary King, Munawar Hafiz, Mohammed Sayagh, Bram Adams, and Abram Hindle. 2016. Energy profiles of java collections classes. In Proc. of the 38th Int. Conf. on Software Engineering. ACM, 225--236.
11. Andrei Homescu and Alex Şuhan. 2011. HappyJIT: A Tracing JIT Compiler for PHP. SIGPLAN Not. 47, 2 (Oct. 2011), 25--36.
12. Reyhaneh Jabbarvand, Alireza Sadeghi, Hamid Bagheri, and Sam Malek. 2016. Energy-aware Test-suite Minimization for Android Apps. In Proc. of the 25th Int. Symposium on Software Testing and Analysis (ISSTA 2016). 425--436.
13. Ding Li, Shuai Hao, William GJ Halfond, and Ramesh Govindan. 2013. Calculating source line level energy information for android applications. In Proc. of the 2013 Int. Symposium on Software Testing and Analysis. ACM, 78--89.
14. Ding Li, Yuchen Jin, Cagri Sahin, James Clause, and William GJ Halfond. 2014. Integrated energy-directed test suite optimization. In Proc. of the 2014 Int. Symposium on Software Testing and Analysis. ACM, 339--350.
15. Wing Hang Li, David R. White, and Jeremy Singer. 2013. JVM-hosted Languages: They Talk the Talk, but Do They Walk the Walk?. In Proc. of the 2013 Int. Conf. on Principles and Practices of Programming on the Java Platform: Virtual Machines, Languages, and Tools (PPPJ '13). ACM, 101--112.
16. Luís Gabriel Lima, Gilberto Melfe, Francisco Soares-Neto, Paulo Lieuthier, João Paulo Fernandes, and Fernando Castor. 2016. Haskell in Green Land: Analyzing the Energy Behavior of a Purely Functional Language. In Proc. of the 23rd IEEE Int. Conf. on Software Analysis, Evolution, and Reengineering (SANER'2016). IEEE, 517--528.
17. Mario Linares-Vásquez, Gabriele Bavota, Carlos Bernal-Cárdenas, Rocco Oliveto, Massimiliano Di Penta, and Denys Poshyvanyk. 2014. Mining energy-greedy API usage patterns in Android apps: an empirical study. In Proc. of the 11th Working Conf. on Mining Software Repositories. ACM, 2--11.
18. Kenan Liu, Gustavo Pinto, and Yu David Liu. 2015. Data-oriented characterization of application-level energy optimization. In Fundamental Approaches to Software Engineering. Springer, 316--331.
19. Dustin McIntire, Thanos Stathopoulos, Sasank Reddy, Thomas Schmidt, and William J. Kaiser. 2012. Energy-Efficient Sensing with the Low Power, Energy Aware Processing (LEAP) Architecture. ACM Trans. Embed. Comput. Syst. 11, 2, Article 27 (July 2012), 36 pages.
20. Floréal Morandat, Brandon Hill, Leo Osvald, and Jan Vitek. 2012. Evaluating the Design of the R Language: Objects and Functions for Data Analysis. In Proc. of the 26th European Conf. on Object-Oriented Programming (ECOOP'12). Springer-Verlag, 104--131.
21. Rui Pereira, Tiago Carção, Marco Couto, Jácome Cunha, João Paulo Fernandes, and João Saraiva. 2017. Helping Programmers Improve the Energy Efficiency of Source Code. In Proceedings of the 39th International Conference on Software Engineering Companion (ICSE-C '17). 238--240.
22. Rui Pereira, Marco Couto, João Saraiva, Jácome Cunha, and João Paulo Fernandes. 2016. The Influence of the Java Collection Framework on Overall Energy Consumption. In Proc. of the 5th Int. Workshop on Green and Sustainable Software (GREENS '16). ACM, 15--21.
23. Gustavo Pinto, Fernando Castor, and Yu David Liu. 2014. Mining questions about software energy consumption. In Proc. of the 11th Working Conf. on Mining Software Repositories. ACM, 22--31.
24. Gustavo Pinto, Fernando Castor, and Yu David Liu. 2014. Understanding energy behaviors of thread management constructs. In Proc. of the 2014 ACM Int. Conf. on Object Oriented Programming Systems Languages & Applications. ACM, 345--360.
25. G. Pinto, K. Liu, F. Castor, and Y. D. Liu. 2016. A Comprehensive Study on the Energy Efficiency of Java's Thread-Safe Collections. (Oct 2016), 20--31.
26. Efraim Rotem, Alon Naveh, Avinash Ananthakrishnan, Eliezer Weissmann, and Doron Rajwan. 2012. Power-Management Architecture of the Intel Microarchitecture Code-Named Sandy Bridge. IEEE Micro 32, 2 (2012), 20--27.
27. Cagri Sahin, Furkan Cayci, Irene Lizeth Manotas Gutierrez, James Clause, Fouad Kiamilev, Lori Pollock, and Kristina Winbladh. 2012. Initial explorations on design pattern energy usage. In Green and Sustainable Software (GREENS), 2012 First Int. Workshop on. IEEE, 55--61.
28. Cagri Sahin, Lori Pollock, and James Clause. 2014. How do code refactorings affect energy usage?. In Proc. of the 8th ACM/IEEE Int. Symposium on Empirical Software Engineering and Measurement. ACM, 36.
29. Cagri Sahin, Philip Tornquist, Ryan McKenna, Zachary Pearson, and James Clause. 2014. How Does Code Obfuscation Impact Energy Usage?. In Software Maintenance (ICSM), 2013 29th IEEE Int. Conf. on. IEEE.
30. Vincent St-Amour, Sam Tobin-Hochstadt, and Matthias Felleisen. 2012. Optimization Coaching: Optimizers Learn to Communicate with Programmers. In Proc. of the ACM Int. Conf. on Object Oriented Programming Systems Languages and Applications (OOPSLA '12). ACM, 163--178.
31. Anne E Trefethen and Jeyarajan Thiyagalingam. 2013. Energy-aware software: Challenges, opportunities and strategies. Journal of Computational Science 4, 6 (2013), 444--449.
32. Kevin Williams, Jason McCandless, and David Gregg. 2010. Dynamic Interpretation for Dynamic Scripting Languages. In Proc. of the 8th Annual IEEE/ACM Int. Symposium on Code Generation and Optimization (CGO '10). ACM, 278--287.
33. Tomofumi Yuki and Sanjay Rajopadhye. 2014. Folklore confirmed: Compiling for speed= compiling for energy. In Languages and Compilers for Parallel Computing. Springer, 169--184.
34. Lide Zhang, Birjodh Tiwana, Zhiyun Qian, Zhaoguang Wang, Robert P. Dick, Zhuoqing Morley Mao, and Lei Yang. Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In Proc. of the 8th Int. Conf. on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2010, part of ESWeek '10 Sixth Embedded Systems Week, Scottsdale, AZ, USA, October 24--28, 2010.

Index Terms

• Software and its engineering

  • Software notations and tools

    • General programming languages

  • Software organization and properties

    • Extra-functional properties

      • Software performance