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The Computer Language Benchmark Game in Papers

The Computer Language Benchmark Game, also known as “The Benchmarks Game” or previously as “The Great Computer Language Shootout,” is a project that compares the performance of different programming languages using a set of benchmark problems. It’s a valuable resource for researchers comparing language performance.

Here are a few papers from recent years that have utilized CLBG:

  1. “An Empirical Study on the Energy Consumption of Python Idioms” (2021) by Oliveira et al. This paper used CLBG to study the energy efficiency of different Python programming styles.
  2. “Performance and Energy Efficiency Across Programming Languages” (2021) by Pereira et al. This study used CLBG to compare the performance and energy consumption of various programming languages.
  3. “Automated Detection of Performance Regressions Using Statistical Learning Techniques” (2020) by Laaber et al. While not exclusively about CLBG, this paper used it as part of their methodology for detecting performance regressions in software.

Studies on Programming Language Energy Efficiency

Recent scientific journals that discuss energy measurement in programming languages include:

  1. “Energy efficiency across programming languages: how do energy, time, and memory relate?” by Pereira et al. (2017) and its follow-up in 2021, which uses the Computer Language Benchmarks Game (CLBG) to compare the energy efficiency of various programming languages (https://dl.acm.org/doi/10.1145/3136014.3136031) (https://www.devsustainability.com/p/paper-notes-energy-efficiency-across-programming-languages)
  2. “Ranking programming languages by energy efficiency” by Pereira et al. (2021), which validates the 2017 results using a more “real-world” analysis on a codebase that better represents day-to-day programming problems (https://www.devsustainability.com/p/paper-notes-energy-efficiency-across-programming-languages)
  3. “Analyzing Programming Languages’ Energy Consumption: An Empirical Study” on ResearchGate, which compares the energy consumption of interpreted programming languages like PHP, Ruby, and JavaScript with languages like Swift, R, Perl, and Python (https://www.researchgate.net/publication/321415912_Analyzing_Programming_Languages%27_Energy_Consumption_An_Empirical_Study)
  4. Analysis of programming languages used in solving energy problems” by Pelagie Flore Temgoua Nanfack et al., which discusses the use of programming languages in energy engineering and identifies C++ and Python as the most used GPLs, with Julia, R, and Matlab as the most prominent DSLs (https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/21/e3sconf_enrconf2021_01006.pdf) (https://www.researchgate.net/publication/361973044_Analysis_of_programming_languages_used_in_solving_energy_problems)

These studies provide insights into the energy efficiency of various programming languages, focusing on both theoretical benchmarks and practical applications in the field of energy engineering.

Recent References on Programming Language Energy Efficiency

These papers cover various aspects of the topic, including:

  1. Ranking programming languages by energy efficiency
  2. Energy characteristics of specific APIs and languages (e.g., Java, Haskell)
  3. Energy patterns for mobile applications
  4. Manifestos and best practices for energy-aware software development
  5. Case studies and empirical evaluations of energy efficiency in software engineering
  6. Tools and techniques for estimating and measuring software energy consumption
  7. Practitioners’ perspectives on green software engineering
  8. Comparisons of energy consumption in different programming contexts

Recent References on Energy Efficiency in Programming Languages (Last 5 Years)

  1. Pereira, R., Couto, M., Ribeiro, F., Rua, R., Cunha, J., Fernandes, J. P., & Saraiva, J. (2021). Ranking Programming Languages by Energy Efficiency. https://www.sciencedirect.com/science/article/abs/pii/S0167642321000022 .
  2. Zakaria Ournani, Evaluating The Energy Consumption of Java I/O APIs https://ieeexplore.ieee.org/document/9609210
  3. Cruz, L., Abreu, R. Catalog of energy patterns for mobile applications. Empir Software Eng 24, 2209–2235 (2019). https://doi.org/10.1007/s10664-019-09682-0
  4. Fonseca, A., Kazman, R., & Lago, P. (2019). A manifesto for energy-aware software. https://ieeexplore.ieee.org/document/8880037
  5. Luís Gabriel Lima, Francisco Soares-Neto, Paulo Lieuthier, Fernando Castor, Gilberto Melfe, João Paulo Fernandes, On Haskell and energy efficiency,
    Journal of Systems and Software, https://doi.org/10.1016/j.jss.2018.12.014.
    (https://www.sciencedirect.com/science/article/pii/S0164121218302747)
  6. R. Verdecchia, G. Procaccianti, I. Malavolta, P. Lago and J. Koedijk, “Estimating Energy Impact of Software Releases and Deployment Strategies: The KPMG Case Study,” 2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), Toronto, ON, Canada, 2017, pp. 257-266, doi: 10.1109/ESEM.2017.39.
  7. Chowdhury, S., Borle, S., Romansky, S. et al. GreenScaler: training software energy models with automatic test generation. Empir Software Eng 24, 1649–1692 (2019). https://doi.org/10.1007/s10664-018-9640-7
  8. An empirical study of practitioners’ perspectives on green software engineering https://dl.acm.org/doi/10.1145/2884781.2884810
  9. Procaccianti, G., Fernández, H., & Lago, P. (2019). Empirical evaluation of two best practices for energy-efficient software development. Journal of Systems and Software, 147, 64-80. (https://research.vu.nl/en/publications/empirical-evaluation-of-two-best-practices-for-energy-efficient-s)
  10. Hamizi, I., Bakare, A., Fraz, K., Dlamini, G., Kholmatova, Z. (2021). A Meta-analytical Comparison of Energy Consumed by Two Different Programming Languages. In: Succi, G., Ciancarini, P., Kruglov, A. (eds) Frontiers in Software Engineering. ICFSE 2021. Communications in Computer and Information Science, vol 1523. Springer, Cham. https://doi.org/10.1007/978-3-030-93135-3_12

Difference between Systematic vs. Literature Review

 
  Systematic Review Literature Review
Definition High-level overview of primary research on a focused question that identifies, selects, synthesizes, and appraises all high quality research evidence relevant to that question Qualitatively summarizes evidence on a topic using informal or subjective methods to collect and interpret studies
Goals Answers a focused clinical question
Eliminate bias		 Provide summary or overview of topic
Question Clearly defined and answerable clinical question
Recommend using PICO as a guide		 Can be a general topic or a specific question
Components Pre-specified eligibility criteria
Systematic search strategy
Assessment of the validity of findings
Interpretation and presentation of results
Reference list		 Introduction
Methods
Discussion
Conclusion
Reference list
Number of Authors Three or more One or more
Timeline Months to years
Average eighteen months		 Weeks to months
Requirement Thorough knowledge of topic
Perform searches of all relevant databases
Statistical analysis resources (for meta-analysis)

Understanding of topic
Perform searches of one or more databases
Value Connects practicing clinicians to high quality evidence
Supports evidence-based practice		 Provides summary of literature on the topic

Source: Lynn Kysh, What’s in a name? The difference between a Systematic Review and a Literature Review, and why it matters

20 Pola Pemrograman Dinamis

20 dynamic programming https://blog.algomaster.io/p/20-patterns-to-master-dynamic-programming

  1. Fibonacci Sequence
  2. Kadane’s Algorithm
  3. 0/1 Knapsack
  4. Unbounded Knapsack
  5. Longest Common Subsequence (LCS)
  6. Longest Increasing Subsequence (LIS)
  7. Palindromic Subsequence
  8. Edit Distance
  9. Subset Sum
  10. String Partition
  11. Catalan Numbers
  12. Matrix Chain Multiplication
  13. Count Distinct Ways
  14. DP on Grids
  15. DP on Trees
  16. DP on Graphs
  17. Digit DP
  18. Bitmasking DP
  19. Probability DP
  20. State Machine DP

Komponen di senjata Rusia

Senjata canggih buatan Rusia menggunakan komponen-komponen dari blok barat (Amerika, Eropa, Jepang, dsb)

Sumber:

  • https://www.ft.com/content/ef463ac9-4804-4ad7-b9a2-c113590f2f96
  • https://en.defence-ua.com/analysis/important_detail_about_western_electronics_in_russian_missiles_revealed_typical_circuit_boards_from_a_small_number_of_suppliers-4114.html
  • https://www.pravda.com.ua/eng/news/2024/07/10/7464966/
  • https://www.themoscowtimes.com/2024/07/10/western-parts-power-russian-missile-that-hit-kyiv-childrens-hospital-ft-a85669

Recovery Hard Disk dengan ddrescue

Jika ada hard disk yang rusak , untuk proses recovery tahap pertama adalah melakukan copy hard disk rusak itu ke hard disk lain. Setelah itu baru utak-atik hasil copy tersebut.

Proses copy hard disk sector-by-sector yang mudah adalah menggunakan aplikasi ‘ddrescue’ di Ubuntu

Berikut ini perintah untuk menjalankan ddrescue

sudo ddrescue -d -r3 –force /dev/sdc /dev/sdb /home/admin/rescue.log