This work investigates if best coding practices in Java can stand for eco-design rules as they deal with software performance. It focuses on how validating such an hypothesis for consumed energy, spent execution time and peak allocated memory. It leads to this silent feedback: no need to carry on many measures.
Devwork.vn xin giới thiệu tài liệu về .NET và Visual Studio
Giới thiệu về các kiến thức lập trình cơ bản trên ngôn ngữ C#
Kiểu dữ liệu
Toán tử
Thao tác với chuỗi Cấu trúc điều khiển phương thức
Xử lý ngoại lệ
Dạy học định lí toán học ở trường trung học phổ thông theo hướng tăng cường r...thuvienso24h
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http://catalog.vnulib.edu.vn/primo_library/libweb/action/search.do
?page=azbooks
http://tvs.vinhuni.edu.vn/Default.aspx
Devwork.vn xin giới thiệu tài liệu về .NET và Visual Studio
Giới thiệu về các kiến thức lập trình cơ bản trên ngôn ngữ C#
Kiểu dữ liệu
Toán tử
Thao tác với chuỗi Cấu trúc điều khiển phương thức
Xử lý ngoại lệ
Dạy học định lí toán học ở trường trung học phổ thông theo hướng tăng cường r...thuvienso24h
Bạn đang xem miễn phí một phần cuốn sách. Để đọc toàn bộ cuốn sách
vui lòng truy cập http://www.thuvienso24h.tk/ để Download.
Chúng tôi hỗ trợ bạn download mọi tài liệu từ 5 thư viện số :
http://thuvienso.hcmutrans.edu.vn
http://thuvien.hcmutrans.edu.vn/
http://www.vnulib.edu.vn:8000/dspace/
http://catalog.vnulib.edu.vn/primo_library/libweb/action/search.do
?page=azbooks
http://tvs.vinhuni.edu.vn/Default.aspx
This document provides relevant and useful information about Java strings. It covers a brief description about strings and how to create a string in Java. It also covers detailed information on String, StringBuffer, and StringBuilder classes and differences between them.
(Video and more at fsharpforfunandprofit.com/csharp)
Curious about F# and want to understand how is it different from C#?
In this talk, we'll look at the basics of coding in F#, and how functional programming differs from object-oriented programming. Along the way, there will be many examples showing the same code written in C# and F# so that you can see for yourself how the two languages differ in style and approach.
While the Python logging module makes it simple to add flexible logging to your application, wording log messages and choosing the appropriate level to maximize their helpfulness is a topic hardly covered in the documentation. This talk give guidelines on when to choose a certain log level, what information to include and which wording templates to use.
A talk about Type hints in python 3 and the type checker mypy.
It talks about typing module, gradual typing, type checkers and how mypy can be used for type checking.
Overview of Structural Subtyping, brief explanation of Python Protocols and example.
Python too supports file handling and allows users to handle files i.e., to read and write files, along with many other file handling options, to operate on files. The concept of file handling has stretched over various other languages, but the implementation is either complicated or lengthy, but alike other concepts of Python, this concept here is also easy and short. Python treats file differently as text or binary and this is important. Each line of code includes a sequence of characters and they form text file. Each line of a file is terminated with a special character, called the EOL or End of Line characters like comma {,} or newline character. It ends the current line and tells the interpreter a new one has begun. Let’s start with Reading and Writing files.
Designing a web API is hard, designing a mobile API is even harder. With heavy constraints such as bandwidth, latency and CPU power, developing a mobile API is a challenge for the service provider and the application developer. As mobile devices become ubiquitous and connected, offering the best user experience in mobile application is crucial; optimizing the network is an important part of it.
In this talk we'll cover the challenges of designing a mobile API as well as innovative solutions and best practices that can be used by the service provider. We'll share our broad experience in developing connected mobile apps.
YouTube Link: https://youtu.be/T6uCFDRVoRE
** Python Certification Training: https://www.edureka.co/python **
This Edureka Python JSON PPT will introduce you to JSON in Python and how you can do Parsing with various other operations.
The session will focus on pointers like:
Introduction to JSON in Python
Why do we use JSON?
Parsing JSON
Coding Demonstration
Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
An introduction to Jupyter notebooks and the Noteable serviceJisc
A presentation at Connect More in Scotland, 4 June 2019.
Speaker: James Slack, e-learning officer for computational notebooks DLAM, University of Edinburgh.
Over the past year, the University of Edinburgh has been developing and piloting the Noteable service to help supporting programming and computational teaching.
The Noteable services provide cloud access to Jupyter notebooks; live editable documents that allow you to run code whilst also containing text, data tables and other rich media items such as images and videos. Jupyter allows students to quickly get hands-on with programming content without having to brave an intimidating IDE (integrated development environment) or grapple with the terminal.
This session will give an overview of what Jupyter notebooks are and why they are becoming popular for introductory programming courses. There'll be a discussion around how Jupyter has been adopted at the University of Edinburgh and how the Noteable service has been developed to support computational education.
This presentation about Python Interview Questions will help you crack your next Python interview with ease. The video includes interview questions on Numbers, lists, tuples, arrays, functions, regular expressions, strings, and files. We also look into concepts such as multithreading, deep copy, and shallow copy, pickling and unpickling. This video also covers Python libraries such as matplotlib, pandas, numpy,scikit and the programming paradigms followed by Python. It also covers Python library interview questions, libraries such as matplotlib, pandas, numpy and scikit. This video is ideal for both beginners as well as experienced professionals who are appearing for Python programming job interviews. Learn what are the most important Python interview questions and answers and know what will set you apart in the interview process.
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S. students to programming and computer science. This course will give you hands-on development experience and prepare you for a career as a professional Python programmer.
What is this course about?
The All-in-One Python course enables you to become a professional Python programmer. Any aspiring programmer can learn Python from the basics and go on to master web development & game development in Python. Gain hands on experience creating a flappy bird game clone & website functionalities in Python.
What are the course objectives?
By the end of this online Python training course, you will be able to:
1. Internalize the concepts & constructs of Python
2. Learn to create your own Python programs
3. Master Python Django & advanced web development in Python
4. Master PyGame & game development in Python
5. Create a flappy bird game clone
The Python training course is recommended for:
1. Any aspiring programmer can take up this bundle to master Python
2. Any aspiring web developer or game developer can take up this bundle to meet their training needs
Learn more at https://www.simplilearn.com/mobile-and-software-development/python-development-training
Ce talk est une introduction au Secure Coding pour Java. Il s'efforcera de présenter via différents exemples les bonnes pratiques permettant de développer de manière pragmatique une application java sécurisée. Nous aborderons aussi bien des pratiques fonctionnelles que des morceaux de codes java à erreurs et leur correctifs
This document provides relevant and useful information about Java strings. It covers a brief description about strings and how to create a string in Java. It also covers detailed information on String, StringBuffer, and StringBuilder classes and differences between them.
(Video and more at fsharpforfunandprofit.com/csharp)
Curious about F# and want to understand how is it different from C#?
In this talk, we'll look at the basics of coding in F#, and how functional programming differs from object-oriented programming. Along the way, there will be many examples showing the same code written in C# and F# so that you can see for yourself how the two languages differ in style and approach.
While the Python logging module makes it simple to add flexible logging to your application, wording log messages and choosing the appropriate level to maximize their helpfulness is a topic hardly covered in the documentation. This talk give guidelines on when to choose a certain log level, what information to include and which wording templates to use.
A talk about Type hints in python 3 and the type checker mypy.
It talks about typing module, gradual typing, type checkers and how mypy can be used for type checking.
Overview of Structural Subtyping, brief explanation of Python Protocols and example.
Python too supports file handling and allows users to handle files i.e., to read and write files, along with many other file handling options, to operate on files. The concept of file handling has stretched over various other languages, but the implementation is either complicated or lengthy, but alike other concepts of Python, this concept here is also easy and short. Python treats file differently as text or binary and this is important. Each line of code includes a sequence of characters and they form text file. Each line of a file is terminated with a special character, called the EOL or End of Line characters like comma {,} or newline character. It ends the current line and tells the interpreter a new one has begun. Let’s start with Reading and Writing files.
Designing a web API is hard, designing a mobile API is even harder. With heavy constraints such as bandwidth, latency and CPU power, developing a mobile API is a challenge for the service provider and the application developer. As mobile devices become ubiquitous and connected, offering the best user experience in mobile application is crucial; optimizing the network is an important part of it.
In this talk we'll cover the challenges of designing a mobile API as well as innovative solutions and best practices that can be used by the service provider. We'll share our broad experience in developing connected mobile apps.
YouTube Link: https://youtu.be/T6uCFDRVoRE
** Python Certification Training: https://www.edureka.co/python **
This Edureka Python JSON PPT will introduce you to JSON in Python and how you can do Parsing with various other operations.
The session will focus on pointers like:
Introduction to JSON in Python
Why do we use JSON?
Parsing JSON
Coding Demonstration
Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
An introduction to Jupyter notebooks and the Noteable serviceJisc
A presentation at Connect More in Scotland, 4 June 2019.
Speaker: James Slack, e-learning officer for computational notebooks DLAM, University of Edinburgh.
Over the past year, the University of Edinburgh has been developing and piloting the Noteable service to help supporting programming and computational teaching.
The Noteable services provide cloud access to Jupyter notebooks; live editable documents that allow you to run code whilst also containing text, data tables and other rich media items such as images and videos. Jupyter allows students to quickly get hands-on with programming content without having to brave an intimidating IDE (integrated development environment) or grapple with the terminal.
This session will give an overview of what Jupyter notebooks are and why they are becoming popular for introductory programming courses. There'll be a discussion around how Jupyter has been adopted at the University of Edinburgh and how the Noteable service has been developed to support computational education.
This presentation about Python Interview Questions will help you crack your next Python interview with ease. The video includes interview questions on Numbers, lists, tuples, arrays, functions, regular expressions, strings, and files. We also look into concepts such as multithreading, deep copy, and shallow copy, pickling and unpickling. This video also covers Python libraries such as matplotlib, pandas, numpy,scikit and the programming paradigms followed by Python. It also covers Python library interview questions, libraries such as matplotlib, pandas, numpy and scikit. This video is ideal for both beginners as well as experienced professionals who are appearing for Python programming job interviews. Learn what are the most important Python interview questions and answers and know what will set you apart in the interview process.
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S. students to programming and computer science. This course will give you hands-on development experience and prepare you for a career as a professional Python programmer.
What is this course about?
The All-in-One Python course enables you to become a professional Python programmer. Any aspiring programmer can learn Python from the basics and go on to master web development & game development in Python. Gain hands on experience creating a flappy bird game clone & website functionalities in Python.
What are the course objectives?
By the end of this online Python training course, you will be able to:
1. Internalize the concepts & constructs of Python
2. Learn to create your own Python programs
3. Master Python Django & advanced web development in Python
4. Master PyGame & game development in Python
5. Create a flappy bird game clone
The Python training course is recommended for:
1. Any aspiring programmer can take up this bundle to master Python
2. Any aspiring web developer or game developer can take up this bundle to meet their training needs
Learn more at https://www.simplilearn.com/mobile-and-software-development/python-development-training
Ce talk est une introduction au Secure Coding pour Java. Il s'efforcera de présenter via différents exemples les bonnes pratiques permettant de développer de manière pragmatique une application java sécurisée. Nous aborderons aussi bien des pratiques fonctionnelles que des morceaux de codes java à erreurs et leur correctifs
Java and effective programming. Is it possible? - IAESTE Case Week 2016Łukasz Koniecki
Probably most of use read book "Effective Java" by Joshua Blooch. But what "effective" programming really means? We will go through some real-life problems and talk about possible, effective solutions.
Presentation Joost Visser / SIG - what can be green about software- Workshop ...Jaak Vlasveld
Green IT Amsterdam Region and the Software Improvement Group (SIG) organized the first workshop of the Green Software Community, with as theme 'Green Software Architecture'. For more information, please visit www.greenitamsterdam.nl/greensoftwarecommunity/
This is a presentation by Joost Visser / SIG on what can be green about software
Towards Software Sustainability AssessmentPatricia Lago
After an introduction into the notion of Green IT, green software, and the sustainability role of software applications/systems, this seminar (given at Chalmers University of Technology) presents the idea of the Software Sustainability Assessment method.
Slides of the inaugural speech of Patricia Lago as full professor at the VU University Amsterdam. You can find the accompanying text at: http://dare.ubvu.vu.nl/handle/1871/53978
Do you know, being a Java dev, how to manage development environments with less effort? How to achieve continuous delivery using immutable server concept? How to manage set up a cloud within your workstation and many more? It might be the case you know, I bet it's much more easier to do with Docker.
This is a presentation I did for the new interns at Duo Software which I highlight the pros and cons of being creative and following widely used best practices in software development
Awareness of design smells - indicators of common design problems - helps developers or software engineers understand mistakes made while designing and apply design principles for creating high-quality designs. This workshop provides insights gained from performing refactoring in real-world projects to improve refactoring and reduce the time and costs of managing software projects. The workshop also presents insightful anecdotes and case studies drawn from the trenches of real-world projects. By attending this workshop, you will know pragmatic techniques for refactoring design smells to manage technical debt and to create and maintain high-quality software in practice.
Contents overview:
* Why care about design principles, design quality, or design smells?
* Refactoring as the primary means for repaying technical debt
* Smells that violate abstraction, encapsulation, modularisation, or hierarchy
* Tools and techniques for refactoring
Refactoring for Software Design Smells - Tech Talk Ganesh Samarthyam
Awareness of design smells - indicators of common design problems - helps developers or software engineers understand mistakes made while designing and apply design principles for creating high-quality designs. This workshop provides insights gained from performing refactoring in real-world projects to improve refactoring and reduce the time and costs of managing software projects. The workshop also presents insightful anecdotes and case studies drawn from the trenches of real-world projects. By attending this workshop, you will know pragmatic techniques for refactoring design smells to manage technical debt and to create and maintain high-quality software in practice.
Contents overview:
* Why care about design principles, design quality, or design smells?
* Refactoring as the primary means for repaying technical debt
* Smells that violate abstraction, encapsulation, modularisation, or hierarchy
* Tools and techniques for refactoring
Using SigOpt to Tune Deep Learning Models with Nervana CloudSigOpt
In this talk I'll show how the Bayesian Optimization methods used by SigOpt, coupled with the incredibly scalable deep learning architecture provided with ncloud and neon, allow anyone it easily tune their models to quickly achieve higher accuracy. I'll walk through the techniques and show an explicit example with results.
Formal methods help improve the quality and reliability of software by providing proof of correctness. However, ensuring the correctness of verification tools that apply these formal methods, is itself a much harder problem. A typical way to justify the correctness is to provide soundness proofs based on semantic models. For program verifiers these soundness proofs are quite large and complex. In this thesis, we introduce certified reasoning to provide machine checked proofs of various components of an automated verification system. We develop new certified decision procedures (Omega++) and certified proofs (for compatible sharing) and integrate with an existing automated verification system (HIP/SLEEK). We show that certified reasoning improves the correctness and expressivity of automated verification without sacrificing on performance.
Presentation for Harvard's ABCD Technology in Education group:
The Institute for Quantitative Social Science (IQSS) is a unique entity at Harvard - it combines research, software development, and specialized services to provide innovative solutions to research and scholarship problems at Harvard and beyond. I will talk about the software projects that IQSS is currently working on (Dataverse, Zelig, Consilience, and OpenScholar), including the research and development processes, the benefits provided to the Harvard community, and the impacts on research and scholarship.
Covers basics Artificial neural networks and motivation for deep learning and explains certain deep learning networks, including deep belief networks and autoencoders. It also details challenges of implementing a deep learning network at scale and explains how we have implemented a distributed deep learning network over Spark.
Digicrome- It's a US Based Company that Provides Online Professional Courses. Digicrome is Asia's leading Brand that provides Online Data Science & Artificial intelligence Courses in 60+ Countries like Australia, Canada, America, Singapore, etc. with 50+ live projects & a 100% Job Placement Guarantee in Written with 3 Months stipend Internship
Digicrome is a provider of Job-ready professional Online courses, certification training, and test preparation for people of all ages. We offer comprehensive online training in a variety of subjects, including Data Science, Artificial intelligence, Python, Web Development, Cyber Security, and many more. we emphasize sectors where technology and best practices are fast evolving, and where the demand for qualified personnel far outnumbers supply.
Similar to How Green are Java Best Coding Practices? - GreenDays @ Rennes - 2014-07-01 (20)
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
How Green are Java Best Coding Practices? - GreenDays @ Rennes - 2014-07-01
1. How Green are Java Best Practices?
Best Coding Practices and Software Eco-Design
Jérôme Rocheteau
Institut Catholique d’Arts et Métiers, Nantes, France
GreenDays@Rennes – Mardi 1er juillet 2014
How Green are Java Best Practices? GreenDays | 2014-07-01 1 / 33
2. Overview
1 Eco-Design and Best Practices
2 Formalizing Practices
3 Measuring Codes
4 Analyzing Measures, Codes, Practices
5 Eco-Design Indicators
How Green are Java Best Practices? GreenDays | 2014-07-01 2 / 33
3. Eco-Design and Best Practices
1 Eco-Design and Best Practices
Context and Issues
Hypothesis and Objectives
2 Formalizing Practices
3 Measuring Codes
4 Analyzing Measures, Codes, Practices
5 Eco-Design Indicators
How Green are Java Best Practices? GreenDays | 2014-07-01 3 / 33
4. Context and Issues
Context:
ICT accounted 2% of carbon emissions in 2007
Energy efficiency relies on hardware but not software
Works on energy efficiency classes, energy-aware systems
Issues:
Help developers to build energy-efficient software
1 Detect energy-consuming patterns in source code
2 Replace these patterns by energy-saving ones
Qualify the energy impact for such best practices
How Green are Java Best Practices? GreenDays | 2014-07-01 4 / 33
5. Hypothesis and Objectives
Hypothesis
Best coding practices are eco-design rules
Objectives:
1 Formalizing Java best practices
2 Measuring savings of memory and energy at runtime
3 Evaluating confidence of such results
How Green are Java Best Practices? GreenDays | 2014-07-01 5 / 33
6. Formalizing Practices
1 Eco-Design and Best Practices
2 Formalizing Practices
Informal and Formal Examples
Time, Space, Energy Semantics
3 Measuring Codes
4 Analyzing Measures, Codes, Practices
5 Eco-Design Indicators
How Green are Java Best Practices? GreenDays | 2014-07-01 6 / 33
7. Informal and Formal Examples
Best Coding Practice Example / Informal Rule:
String Literal Initialization
Initialization of strings with the keyword ’new’ creates new
objects. This is costly in time and memory space. Initializations
with literal strings avoids this.
How Green are Java Best Practices? GreenDays | 2014-07-01 7 / 33
8. Informal and Formal Examples
Best Coding Practice Example / Informal Rule:
String Literal Initialization
Initialization of strings with the keyword ’new’ creates new
objects. This is costly in time and memory space. Initializations
with literal strings avoids this.
Three shape of Java best coding practices :
1 Prefer this
2 Avoid that
3 Replace that by this
How Green are Java Best Practices? GreenDays | 2014-07-01 7 / 33
9. Informal and Formal Examples
Best Coding Practice Example / Informal Rule:
String Literal Initialization
Initialization of strings with the keyword ’new’ creates new
objects. This is costly in time and memory space. Initializations
with literal strings avoids this.
Three shape of Java best coding practices :
1 Prefer this
2 Avoid that
3 Replace that by this
Can be extended to other primitive/wrapper types
How Green are Java Best Practices? GreenDays | 2014-07-01 7 / 33
10. Informal and Formal Examples
Best Coding Practice Example / Informal Rule:
String Literal Initialization
Initialization of strings with the keyword ’new’ creates new
objects. This is costly in time and memory space. Initializations
with literal strings avoids this.
Three shape of Java best coding practices :
1 Prefer this
2 Avoid that
3 Replace that by this
Can be extended to other primitive/wrapper types
Can be applied to other programming languages
How Green are Java Best Practices? GreenDays | 2014-07-01 7 / 33
11. Informal and Formal Examples
Best Coding Practice Example / Informal Rule:
String Literal Initialization
Initialization of strings with the keyword ’new’ creates new
objects. This is costly in time and memory space. Initializations
with literal strings avoids this.
Three shape of Java best coding practices :
1 Prefer this
2 Avoid that
3 Replace that by this
Can be extended to other primitive/wrapper types
Can be applied to other programming languages
How Green are Java Best Practices? GreenDays | 2014-07-01 7 / 33
12. Informal and Formal Examples
Listing 1: Prefer String Literal Initialization
<rule id="prefer-string-literal-initialization">
<title>Prefer string literal initialization</title>
<description>
Primitive type objects should be initialized with primitive values
and without the use of any constructors.
</description>
<check green="StringValue" gray="StringObject" />
</rule>
How Green are Java Best Practices? GreenDays | 2014-07-01 8 / 33
13. Informal and Formal Examples
Listing 2: String Literal Initialization
p u b l i c c l a s s S t r i n g V a l u e implements Code {
p r i v a t e S t r i n g [ ] a r r a y ;
p u b l i c void setUp () {
a r r a y = new S t r i n g [ 1 0 0 0 ] ;
}
p u b l i c void doRun () throws Exception {
f o r ( i n t i = 0; i < 1000; i ++) {
a r r a y [ i ] = " abcdefg . . . " ;
}
}
p u b l i c void tearDown () {
a r r a y = n u l l ;
}
}
How Green are Java Best Practices? GreenDays | 2014-07-01 9 / 33
14. Informal and Formal Examples
Listing 3: String Object Initialization
p u b l i c c l a s s S t r i n g O b j e c t implements Code {
p r i v a t e S t r i n g [ ] a r r a y ;
p u b l i c void setUp () {
a r r a y = new S t r i n g [ 1 0 0 0 ] ;
}
p u b l i c void doRun () throws Exception {
f o r ( i n t i = 0; i < 1000; i ++) {
a r r a y [ i ] = new S t r i n g ( " abcdefg . . . " ) ;
}
}
p u b l i c void tearDown () {
a r r a y = n u l l ;
}
}
How Green are Java Best Practices? GreenDays | 2014-07-01 10 / 33
15. Time, Space, Energy Semantics
Prefer Literal
Initialization
time = ft (td , tc )
space = fs (sd , sc )
energy = fe(ed , ec )
How Green are Java Best Practices? GreenDays | 2014-07-01 11 / 33
16. Time, Space, Energy Semantics
String Value
Initialization
Prefer Literal
Initialization
String Object
Initialization
time = ft (td , tc )
space = fs (sd , sc )
energy = fe(ed , ec )
How Green are Java Best Practices? GreenDays | 2014-07-01 11 / 33
17. Time, Space, Energy Semantics
String Value
Initialization
Prefer Literal
Initialization
String Object
Initialization
time tc
space sc
energy ec
time td
space sd
energy ed
time = ft (td , tc )
space = fs (sd , sc )
energy = fe(ed , ec )
How Green are Java Best Practices? GreenDays | 2014-07-01 11 / 33
18. Time, Space, Energy Semantics
String Value
Initialization
Prefer Literal
Initialization
String Object
Initialization
time tc
space sc
energy ec
time td
space sd
energy ed
time = ft (td , tc )
space = fs (sd , sc )
energy = fe(ed , ec )
How Green are Java Best Practices? GreenDays | 2014-07-01 11 / 33
19. Time, Space, Energy Semantics
Goal: statistical static analysis method and tools
original code
How Green are Java Best Practices? GreenDays | 2014-07-01 12 / 33
20. Time, Space, Energy Semantics
Goal: statistical static analysis method and tools
original code
How Green are Java Best Practices? GreenDays | 2014-07-01 12 / 33
21. Time, Space, Energy Semantics
Goal: statistical static analysis method and tools
original code refactored code
How Green are Java Best Practices? GreenDays | 2014-07-01 12 / 33
22. Time, Space, Energy Semantics
Goal: statistical static analysis method and tools
original code refactored code
possible savings
X joules / X %
How Green are Java Best Practices? GreenDays | 2014-07-01 12 / 33
23. Measuring Codes
1 Eco-Design and Best Practices
2 Formalizing Practices
3 Measuring Codes
Needs and Requirements
Measure Task and Process
4 Analyzing Measures, Codes, Practices
5 Eco-Design Indicators
How Green are Java Best Practices? GreenDays | 2014-07-01 13 / 33
25. Needs and Requirements
Needs:
power-meter with digital outputs
Requirements:
power-meter with fine-grain precision
How Green are Java Best Practices? GreenDays | 2014-07-01 14 / 33
26. Needs and Requirements
Needs:
power-meter with digital outputs
memory monitor with digital outputs
Requirements:
power-meter with fine-grain precision
How Green are Java Best Practices? GreenDays | 2014-07-01 14 / 33
27. Needs and Requirements
Needs:
power-meter with digital outputs
memory monitor with digital outputs
platform for running Java codes
Requirements:
power-meter with fine-grain precision
Java micro-benchmarking to avoid JIT
How Green are Java Best Practices? GreenDays | 2014-07-01 14 / 33
28. Needs and Requirements
Needs:
power-meter with digital outputs
memory monitor with digital outputs
platform for running Java codes
system for managing codes and measures
Requirements:
power-meter with fine-grain precision
Java micro-benchmarking to avoid JIT
system able to manage physical and logical sensors
How Green are Java Best Practices? GreenDays | 2014-07-01 14 / 33
29. Measure Task and Process
Measurement Protocol:
1 4 seconds idle
2 10 seconds execution time
3 3 seconds idle
How Green are Java Best Practices? GreenDays | 2014-07-01 15 / 33
30. Measure Task and Process
Semantics based on quantitative metrics:
x-axis execution time
y-axis instant power or instant memory space
How Green are Java Best Practices? GreenDays | 2014-07-01 15 / 33
31. Measure Task and Process
Preliminary Computations:
How Green are Java Best Practices? GreenDays | 2014-07-01 15 / 33
32. Measure Task and Process
Preliminary Computations:
total energy obtained by the trapezoidal rule
How Green are Java Best Practices? GreenDays | 2014-07-01 15 / 33
33. Measure Task and Process
Preliminary Computations:
total energy obtained by the trapezoidal rule
idle power = average of the first 4 second instant powers
How Green are Java Best Practices? GreenDays | 2014-07-01 15 / 33
34. Measure Task and Process
Preliminary Computations:
total energy obtained by the trapezoidal rule
idle power = average of the first 4 second instant powers
idle energy = idle power × protocol time
How Green are Java Best Practices? GreenDays | 2014-07-01 15 / 33
35. Measure Task and Process
Code energy computation and normalization:
code energy = total energy - idle energy
How Green are Java Best Practices? GreenDays | 2014-07-01 15 / 33
36. Measure Task and Process
Code energy computation and normalization:
code energy = total energy - idle energy
normalized code energy = code energy / times of execution
How Green are Java Best Practices? GreenDays | 2014-07-01 15 / 33
37. Measure Task and Process
Measure Process:
How Green are Java Best Practices? GreenDays | 2014-07-01 16 / 33
38. Measure Task and Process
Measure Process:
1 Retrieve an available Java code
How Green are Java Best Practices? GreenDays | 2014-07-01 16 / 33
39. Measure Task and Process
Measure Process:
1 Retrieve an available Java code
2 Iterate while this code isn’t mature:
How Green are Java Best Practices? GreenDays | 2014-07-01 16 / 33
40. Measure Task and Process
Measure Process:
1 Retrieve an available Java code
2 Iterate while this code isn’t mature:
Clean its measure set
How Green are Java Best Practices? GreenDays | 2014-07-01 16 / 33
41. Measure Task and Process
Measure Process:
1 Retrieve an available Java code
2 Iterate while this code isn’t mature:
Clean its measure set
Compute its maturity
How Green are Java Best Practices? GreenDays | 2014-07-01 16 / 33
42. Measure Task and Process
Measure Process:
1 Retrieve an available Java code
2 Iterate while this code isn’t mature:
Clean its measure set
Compute its maturity
Plan new measures if required
How Green are Java Best Practices? GreenDays | 2014-07-01 16 / 33
43. Measure Task and Process
Measure Process:
1 Retrieve an available Java code
2 Iterate while this code isn’t mature:
Clean its measure set
Compute its maturity
Plan new measures if required
Perform these measures
3 Send the report of this code
How Green are Java Best Practices? GreenDays | 2014-07-01 16 / 33
44. Analyzing Measures, Codes, Practices
1 Eco-Design and Best Practices
2 Formalizing Practices
3 Measuring Codes
4 Analyzing Measures, Codes, Practices
Clean and Canonical Measures
Code Maturity
Results
5 Eco-Design Indicators
How Green are Java Best Practices? GreenDays | 2014-07-01 17 / 33
45. Clean and Canonical Measures
3 kinds of measure disturbances:
How Green are Java Best Practices? GreenDays | 2014-07-01 18 / 33
46. Clean and Canonical Measures
3 kinds of measure disturbances:
disturbances before the measure task underestimate
How Green are Java Best Practices? GreenDays | 2014-07-01 18 / 33
47. Clean and Canonical Measures
3 kinds of measure disturbances:
disturbances before the measure task underestimate
disturbances after the measure task overestimate
How Green are Java Best Practices? GreenDays | 2014-07-01 18 / 33
48. Clean and Canonical Measures
3 kinds of measure disturbances:
disturbances before the measure task underestimate
disturbances after the measure task overestimate
disturbances during the measure task overestimate
How Green are Java Best Practices? GreenDays | 2014-07-01 18 / 33
49. Clean and Canonical Measures
3 kinds of measure disturbances:
disturbances before the measure task
disturbances after the measure task
disturbances during the measure task
2 mere algorithms for cleaning measures:
How Green are Java Best Practices? GreenDays | 2014-07-01 19 / 33
50. Clean and Canonical Measures
3 kinds of measure disturbances:
disturbances before the measure task
disturbances after the measure task
disturbances during the measure task
2 mere algorithms for cleaning measures:
bounds checking algorithm (over a single measure)
How Green are Java Best Practices? GreenDays | 2014-07-01 19 / 33
51. Clean and Canonical Measures
3 kinds of measure disturbances:
disturbances before the measure task
disturbances after the measure task
disturbances during the measure task
2 mere algorithms for cleaning measures:
bounds checking algorithm (over a single measure)
split-and-merge algorithm (over a set of measures)
How Green are Java Best Practices? GreenDays | 2014-07-01 19 / 33
52. Clean and Canonical Measures
Cleaning algorithm evaluation:
500 clean measures from 25 rules annotated by 3 experts
How Green are Java Best Practices? GreenDays | 2014-07-01 20 / 33
53. Clean and Canonical Measures
Cleaning algorithm evaluation:
500 clean measures from 25 rules annotated by 3 experts
inter-agreement κ: 0.94 (almost perfect)
How Green are Java Best Practices? GreenDays | 2014-07-01 20 / 33
54. Clean and Canonical Measures
Cleaning algorithm evaluation:
500 clean measures from 25 rules annotated by 3 experts
inter-agreement κ: 0.94 (almost perfect)
baseline = quartile method
precision: 0.953
recall: 0.911
How Green are Java Best Practices? GreenDays | 2014-07-01 20 / 33
55. Clean and Canonical Measures
Cleaning algorithm evaluation:
500 clean measures from 25 rules annotated by 3 experts
inter-agreement κ: 0.94 (almost perfect)
baseline = quartile method
precision: 0.953
recall: 0.911
split-and-merge algorithm
precision: 0.941
recall: 1.0
How Green are Java Best Practices? GreenDays | 2014-07-01 20 / 33
56. Clean and Canonical Measures
Cleaning algorithm evaluation:
500 clean measures from 25 rules annotated by 3 experts
inter-agreement κ: 0.94 (almost perfect)
baseline = quartile method
precision: 0.953
recall: 0.911
split-and-merge algorithm
precision: 0.941
recall: 1.0
remove all disturbed measures
How Green are Java Best Practices? GreenDays | 2014-07-01 20 / 33
57. Clean and Canonical Measures
Canonical measure:
How Green are Java Best Practices? GreenDays | 2014-07-01 21 / 33
58. Clean and Canonical Measures
Canonical measure:
time: t3
space: s3
energy: e3
time: t2
space: s2
energy: e2
time: t1
space: s1
energy: e1
How Green are Java Best Practices? GreenDays | 2014-07-01 21 / 33
59. Clean and Canonical Measures
Canonical measure:
time: t3
space: s3
energy: e3
time: t2
space: s2
energy: e2
time: t1
space: s1
energy: e1
time: t
space: s
energy: e
How Green are Java Best Practices? GreenDays | 2014-07-01 21 / 33
60. Clean and Canonical Measures
Canonical measure:
time: t3
space: s3
energy: e3
time: t2
space: s2
energy: e2
time: t1
space: s1
energy: e1
time: t
space: s
energy: e
How Green are Java Best Practices? GreenDays | 2014-07-01 21 / 33
61. Clean and Canonical Measures
Canonical measure:
time: t3
space: s3
energy: e3
time: t2
space: s2
energy: e2
time: t1
space: s1
energy: e1
time: t
space: s
energy: e
How Green are Java Best Practices? GreenDays | 2014-07-01 21 / 33
62. Clean and Canonical Measures
Canonical measure:
time: t3
space: s3
energy: e3
time: t2
space: s2
energy: e2
time: t1
space: s1
energy: e1
time: t
space: s
energy: e
error margin: 2%
How Green are Java Best Practices? GreenDays | 2014-07-01 21 / 33
63. Code Maturity
Code Maturity
How many clean measures required for reliable results?
How Green are Java Best Practices? GreenDays | 2014-07-01 22 / 33
64. Code Maturity
Code Maturity
How many clean measures required for reliable results?
0 20 40 60 80 100 120
5
10
15
Measure Number
RelativeStandardDeviation
How Green are Java Best Practices? GreenDays | 2014-07-01 22 / 33
65. Code Maturity
Code Maturity
How many clean measures required for reliable results?
0 20 40 60 80 100 120
5
10
15
Measure Number
RelativeStandardDeviation
a set of 25 measures minimum
How Green are Java Best Practices? GreenDays | 2014-07-01 22 / 33
66. Code Maturity
Code Maturity
How many clean measures required for reliable results?
0 20 40 60 80 100 120
5
10
15
Measure Number
RelativeStandardDeviation
a set of 25 measures minimum
a standard deviation less than 10%
How Green are Java Best Practices? GreenDays | 2014-07-01 22 / 33
67. Results
Energy in nanojoules, time in nanoseconds
Memory in kilobytes
Standard Deviation on Energy
Replace Object Initialization by Literal Initialization
Rule Id
GreenEnergy
GrayEnergy
GreenTime
GrayTime
GreenMemory
GrayMemory
GreenStdDev
GrayStdDev
String 697 7885 842 7827 4136 36104 4.40% 8.14%
Float 10311 10448 4736 4127 20032 20032 5.85% 6.58%
Integer 685 9575 833 4591 4048 20032 5.21% 6.51%
Boolean 683 6267 775 4741 4048 20032 4.77% 7.03%
Char 695 33067 840 4595 4048 20032 5.04% 4.65%
Double 10003 10210 5810 4270 28032 28032 5.58% 7.83%
Long 669 8236 807 6066 4056 28032 2.89% 5.32%
Short 680 7819 819 3846 4048 20031 4.87% 7.71%
How Green are Java Best Practices? GreenDays | 2014-07-01 23 / 33
68. Results
Replace Object Initialization by Literal Initialization
Rule Id G
reen
Energy
G
ray
Energy
A
bsolute
G
ain
Relative
G
ain
String 697 7885 7188 91.16%
Float 10311 10448 137 1.31%
Integer 685 9575 8890 92.54%
Boolean 683 6267 5584 89.10%
Char 695 33067 32372 97.89%
Double 10003 10210 207 2.02%
Long 669 8236 7567 91.87%
Short 680 7819 7139 91.30%
How Green are Java Best Practices? GreenDays | 2014-07-01 24 / 33
69. Results
Rules for loops
A Prefer integer loop counters
B Avoid method loop conditions
C Prefer comparison-to-0 conditions
D Prefer first common condition
Id
GreenEnergy
GrayEnergy
GreenTime
GrayTime
GreenMemory
GrayMemory
GreenStdDev
GrayStdDev
A 82 98 4744 5931 16 16 8.66% 7.46%
B 8376 8602 6181 6364 20056 20056 3.83% 6.14%
C 89 284 4709 17367 16 16 5.09% 5.78%
D 97 100 4934 5017 16 16 4.37% 4.60%
How Green are Java Best Practices? GreenDays | 2014-07-01 25 / 33
70. Results
Rules for loops
A Prefer integer loop counters
B Avoid method loop conditions
C Prefer comparison-to-0 conditions
D Prefer first common condition
Id
G
reen
Energy
G
ray
Energy
A
bsolute
G
ain
Relative
G
ain
A 82 98 16 16.32%
B 8376 8602 226 2.62%
C 89 284 195 68.66%
D 97 100 3 3.00%
How Green are Java Best Practices? GreenDays | 2014-07-01 25 / 33
71. Results
Set String Builder or Buffer Size
A String Buffer/Builder capacity initialization
B String Buffer/Builder capacity setting
C String Buffer/Builder length setting
Id
GreenEnergy
GrayEnergy
GreenTime
GrayTime
GreenMemory
GrayMemory
GreenStdDev
GrayStdDev
A 593 1053 38085 59111 52056 73784 7.11% 8.40%
B 598 1053 38495 59111 52056 73784 7.86% 8.40%
C 1211 1053 59111 70765 73784 104064 8.40% 9.40%
A’ 378 899 19278 41088 52056 73784 8.27% 7.18%
B’ 429 899 41088 51214 73784 104064 7.18% 5.84%
C’ 1043 899 20976 41088 52056 73784 6.01% 7.18%
How Green are Java Best Practices? GreenDays | 2014-07-01 26 / 33
72. Results
Set String Builder or Buffer Size
A String Buffer/Builder capacity initialization
B String Buffer/Builder capacity setting
C String Buffer/Builder length setting
Id
G
reen
Energy
G
ray
Energy
A
bsolute
G
ain
Relative
G
ain
A 593 1053 460 43.68%
B 598 1053 455 43.20%
C 1211 1053 -158 -15.00%
A’ 378 899 521 57.95%
B’ 429 899 470 52.28%
C’ 1043 899 -144 -16.01%
How Green are Java Best Practices? GreenDays | 2014-07-01 26 / 33
73. Eco-Design Indicators
1 Eco-Design and Best Practices
2 Formalizing Practices
3 Measuring Codes
4 Analyzing Measures, Codes, Practices
5 Eco-Design Indicators
Summary
Future
How Green are Java Best Practices? GreenDays | 2014-07-01 27 / 33
76. Eco-Design Indicators
Hypothesis
Best Coding Practices ≈ Eco-Design Rules
174 formalized and measured rules
55% rules huge savings (more than 10%)
How Green are Java Best Practices? GreenDays | 2014-07-01 29 / 33
77. Eco-Design Indicators
Hypothesis
Best Coding Practices ≈ Eco-Design Rules
174 formalized and measured rules
55% rules huge savings (more than 10%)
20% rules few savings (between 3% and 10%)
How Green are Java Best Practices? GreenDays | 2014-07-01 29 / 33
78. Eco-Design Indicators
Hypothesis
Best Coding Practices ≈ Eco-Design Rules
174 formalized and measured rules
55% rules huge savings (more than 10%)
20% rules few savings (between 3% and 10%)
15% rules no savings (between -3% and 3%)
How Green are Java Best Practices? GreenDays | 2014-07-01 29 / 33
79. Eco-Design Indicators
Hypothesis
Best Coding Practices ≈ Eco-Design Rules
174 formalized and measured rules
55% rules huge savings (more than 10%)
20% rules few savings (between 3% and 10%)
15% rules no savings (between -3% and 3%)
10% rules some losses (less than -3%)
How Green are Java Best Practices? GreenDays | 2014-07-01 29 / 33
80. Eco-Design Indicators
With The Way: 1 reliable measure system
How Green are Java Best Practices? GreenDays | 2014-07-01 30 / 33
81. Eco-Design Indicators
With The Way: 1 reliable measure system
hybrid physical and logical sensor management
How Green are Java Best Practices? GreenDays | 2014-07-01 30 / 33
82. Eco-Design Indicators
With The Way: 1 reliable measure system
hybrid physical and logical sensor management
complex client/server architecture
How Green are Java Best Practices? GreenDays | 2014-07-01 30 / 33
83. Eco-Design Indicators
With The Way: 1 reliable measure system
hybrid physical and logical sensor management
complex client/server architecture
focused star schema database
How Green are Java Best Practices? GreenDays | 2014-07-01 30 / 33
84. Eco-Design Indicators
With The Way: 1 reliable measure system
hybrid physical and logical sensor management
complex client/server architecture
focused star schema database
robust automatic iterative process
How Green are Java Best Practices? GreenDays | 2014-07-01 30 / 33
85. Eco-Design Indicators
With The Way: 1 reliable measure system
hybrid physical and logical sensor management
complex client/server architecture
focused star schema database
robust automatic iterative process
extensible programming API
How Green are Java Best Practices? GreenDays | 2014-07-01 30 / 33
86. Eco-Design Indicators
With The Way: 1 reliable measure system
hybrid physical and logical sensor management
complex client/server architecture
focused star schema database
robust automatic iterative process
extensible programming API
reliable stable measure sets with few measures
How Green are Java Best Practices? GreenDays | 2014-07-01 30 / 33
87. Eco-Design Indicators
• Jérôme Rocheteau, Virginie Gaillard, et Lamya Belhaj.
How Green are Java Best Coding Practices?
Proceedings of the 3rd International Conference on Smart
Grids and Green IT Systems,
pages 235–246.
Barcelona, Espagne, Avril 2014.
How Green are Java Best Practices? GreenDays | 2014-07-01 31 / 33
88. Future
1 Energy Efficiency Classes?
How Green are Java Best Practices? GreenDays | 2014-07-01 32 / 33
89. Future
1 Energy Efficiency Classes?
2 Eco-Design Indicators?
How Green are Java Best Practices? GreenDays | 2014-07-01 32 / 33
90. Future
1 Energy Efficiency Classes?
2 Eco-Design Indicators?
Absolute savings
How Green are Java Best Practices? GreenDays | 2014-07-01 32 / 33
91. Future
1 Energy Efficiency Classes?
2 Eco-Design Indicators?
Absolute savings
Relative savings
How Green are Java Best Practices? GreenDays | 2014-07-01 32 / 33
92. Future
1 Energy Efficiency Classes?
2 Eco-Design Indicators?
Absolute savings
Relative savings
3 Rule indicators in different environments
How Green are Java Best Practices? GreenDays | 2014-07-01 32 / 33
93. Future
1 Energy Efficiency Classes?
2 Eco-Design Indicators?
Absolute savings
Relative savings
3 Rule indicators in different environments
4 Rule indicators in different programming languages
How Green are Java Best Practices? GreenDays | 2014-07-01 32 / 33
94. Future
1 Energy Efficiency Classes?
2 Eco-Design Indicators?
Absolute savings
Relative savings
3 Rule indicators in different environments
4 Rule indicators in different programming languages
5 Energy efficiency of different programming languages
How Green are Java Best Practices? GreenDays | 2014-07-01 32 / 33
95. Future
1 Energy Efficiency Classes?
2 Eco-Design Indicators?
Absolute savings
Relative savings
3 Rule indicators in different environments
4 Rule indicators in different programming languages
5 Energy efficiency of different programming languages
6 Accuracy of different physical and/or logical sensors
How Green are Java Best Practices? GreenDays | 2014-07-01 32 / 33
96. Future
1 Energy Efficiency Classes?
2 Eco-Design Indicators?
Absolute savings
Relative savings
3 Rule indicators in different environments
4 Rule indicators in different programming languages
5 Energy efficiency of different programming languages
6 Accuracy of different physical and/or logical sensors
7 Energy and memory footprints of logical sensors
How Green are Java Best Practices? GreenDays | 2014-07-01 32 / 33