#Fortran programming help

[lboogie1906]

Annie Jean Easley (April 23, 1933 – June 25, 2011) was a computer scientist, mathematician, and rocket scientist. She was born in Birmingham to Samuel and Mary Easley. She sought to be a nurse, but she switched to pharmacy once she started high school.

She entered Xavier University. She completed two years of study she returned to Birmingham. She married a man in the military. She began working as a substitute teacher in Jefferson County, Alabama. She helped members of her community prepare for literacy tests required for voter registration.

She moved to Cleveland where her husband’s family was located. She read an article highlighting twin sisters who worked as “human computers” at the Aircraft Engine Research Laboratory, which was absorbed into NASA. She applied and two weeks later began her 34-year-long career with NASA as a computer scientist and mathematician.

She was one of only four African American employees in the computational section. She was on the front line of space research and subsequent space missions that began with the launch of astronaut John Glenn into orbit in 1962. Her talents were utilized in the Computer Services Division, where some of her earliest work involved running simulations for the Plum Brook Reactor Facility.

She began working on nuclear-powered rocket systems including the Centaur high-energy booster rocket, which had its first successful launch in 1963. She realized that she would need additional training. When NASA gradually replaced its “human computers” with “machine” computers, she learned computer programming languages like Fortran and Simple Object Access Protocol. She returned to school to complete a BS in mathematics from Cleveland State University while working full-time.

She worked with local tutoring programs encouraging younger students to explore their interests in what would be known as the STEM field. She worked as an EEO counselor, addressing race, gender, and age discrimination complaints from NASA employees.

Her legacy continues to inspire countless students to make an impact in the STEM field. #africanhistory365 #africanexcellence

[studyblrspace]

hi! I love your blog. What programming languages would you recommend learning if you wanted to get into astrophysics? I already know c++, but I heard somewhere that python is better for data analysis?

I'm so sorry anon, I drafted an answer and then completely forgot to post it 😭😭😭

the main programming languages I've seen are c++ and python. there's also julia (new), and fortran (🥴).

so its great you already know c++! personally I've used athena++ code for simulations if you wanted a simulation code to play with.

but python would be good to play around with if you're not familiar, its great for visualization / data analysis! I started by learning the syntax and about packages like numpy, matplotlib, and astropy. "Python for Astronomers" may be helpful if you need a resource for learning, it has a free textbook and some tutorials. part of my undergrad computational astrophysics course could was based on it! another fun package is yt, you can look up "python yt cookbook" or click here. this website also gives you sample data from a simulation run and lots of tutorials.

julia is not as widely used but its supposed to have the intuitiveness of python with the performance/speed of c++. it's a newer language, like 10 years old. I've heard that there is a (very slow) shift to this language in the astrophysics community instead of python. I don't have any resources because I haven't gotten around to learning it yet 😅

fortran is an older language, I can't say I'm familiar with it. I've only encountered it in a skeleton simulation code a postdoc was developing (and I was testing the code) so I just know basic syntax. you'd probably be fine not learning it, unless you want to develop your own simulation code soon

I'm only a couple years into (theoretical) astrophysics research so if anyone else has input, please let me know!!

[paladingineer]

The French Fortran Code From Hell

My first job held the grandiose-sounding title of Technical Consulting Engineer, which was a very fancy way of saying "glorified customer support with some additional maintenance tasks."

I was assigned to a piece of software that was used to help people make their code more efficient. Among my duties were: creating documentation and how-to tutorials for the software; answering support tickets for the software; giving lectures on the software; and providing hands-on assistance in what we called Dungeons.

Dungeons were what happened when you locked a bunch of engineers in a windowless room with a handful of TCEs and lunch catering and let them go at their code with the software for the entire duration of the day, completely dead to the outside world - often repeating this process for several days.

I was in quite a few Dungeons during my three horrible years at that soul-sucking job. I want to be clear: the vast majority of this job was the soul-crushing tedium and agony of answering support tickets. The software in question was... not great at providing helpful error messages. 90% of everything resulted in "There's a problem with your license file" even when the license file was fine. So debugging was always an ordeal.

The best ones, strangely enough, were always the tickets from the military, because everything was classified. Anything that happens on their computer is classified. What does the error message say? That's classified. Can you send me the log output? That's classified. What does your license file say on line three? That's classified. You might wonder how this makes anything better for the poor sap trying to debug it. See, when they can't provide you any info, all you can do is send them the entire support script and tell them to go through the steps themselves. Then they message you back a while later politely telling you that it worked and you can now close the ticket. On the other end of the spectrum you had individual customers who bought the software for their own use, and some of these people were real pieces of work. Anyone who has worked customer-facing jobs knows the kind of person I'm talking about.

Anyway, Dungeons were more of an occasional interruption to the never-ending slog of support tickets, and usually a very welcome interruption.

But a couple times it was... let's say interesting. And both of these stories, oddly enough, involve the French.

The first one was the unfortunate time I was in a Dungeon full of engineers who had flown in from France - Paris specifically if I recall correctly. On the 15th of April, 2019. I received a text from my mother with rather alarming news, and thus I had to be the one to inform the room full of French engineers that Notre Dame Cathedral was actively on fire. Needless to say, very little got done that day.

The second one was just plain painful for me. The software in question supports code in both C++ (commonly used language, good) and Fortran (relic from the 1950s, extremely different from most programming languages). It was fairly rare for us to actually deal with the Fortran side of it, however.

But on this occasion, the French engineers I was in a Dungeon with wanted my help optimizing their Fortran code. Fine, I'm not exactly "fluent" but I can probably get the gist of it, I thought.

I was wrong.

You see, this Fortran code was auto-generated. It was not written by human hands and was not intended to be read by human eyes. It contained statements that were hundreds of lines long. Not functions, statements. To those not initiated in programming, this is akin to a run-on sentence that lasts 38 pages. It had variables with such helpful names as xyz and abc. Likewise, for the uninitiated, this is akin to having a pharmacy where all of the bottles are labeled "Medicine, probably."

It had, at some point, been minimally edited, or at least annotated, by humans, however. Because there were a very small handful of comments!

...Which were in French.

I do not speak French.

The French engineers did not know how to translate French Jargon into English.

Obviously, our company did not ever want us to say "we can't." But in this one case, nobody took issue when I looked these French engineers in the eye and just told them "I'm sorry, but this code is beyond our ability to optimize. It is beyond anyone's ability to optimize. It must be cast into the fire and destroyed; and may god help you."

[fromdevcom]

I decided to write this article when I realized what a great step forward the modern computer science learning has done in the last 20 years. Think of it. My first “Hello, world” program was written in Sinclair BASIC in 1997 for КР1858ВМ1r This dinosaur was the Soviet clone of the Zilog Z80 microprocessor and appeared on the Eastern Europe market in 1992-1994. I didn’t have any sources of information on how to program besides the old Soviet “Encyclopedia of Dr. Fortran”. And it was actually a graphic novel rather than a BASIC tutorial book. This piece explained to children how to sit next to a monitor and keep eyesight healthy as well as covered the general aspects of programming. Frankly, it involved a great guesswork but I did manage to code. The first real tutorial book I took in my hands in the year of 2000 was “The C++ Programming Language” by Bjarne Stroustrup, the third edition. The book resembled a tombstone and probably was the most fundamental text for programmers I’d ever seen. Even now I believe it never gets old. Nowadays, working with such technologies as Symfony or Django in the DDI Development software company I don’t usually apply to books because they become outdated before seeing a printing press. Everyone can learn much faster and put a lesser effort into finding new things. The number of tutorials currently available brings the opposite struggle to what I encountered: you have to pick a suitable course out of the white noise. In order to save your time, I offer the 20 best tutorials services for developers. Some of them I personally use and some have gained much recognition among fellow technicians. Lynda.com The best thing about Lynda is that it covers all the aspects of web development. The service currently has 1621 courses with more than 65 thousand videos coming with project materials by experts. Once you’ve bought a monthly subscription for a small $30 fee you get an unlimited access to all tutorials. The resource will help you grow regardless your expertise since it contains and classifies courses for all skill levels. Pluralsight.com Another huge resource with 1372 courses currently available from developers for developers. It may be a hardcore decision to start with Pluralsight if you’re a beginner, but it’s a great platform to enhance skills if you already have some programming background. A month subscription costs the same $30 unless you want to receive downloadable exercise files and additional assessments. Then you’ll have to pay $50 per month. Codecademy.com This one is great to start with for beginners. Made in an interactive console format it leads you through basic steps to the understanding of major concepts and techniques. Choose the technology or language you like and start learning. Besides that, Codecademy lets you build websites, games, and apps within its environment, join the community and share your success. Yes, and it’s totally free! Probably the drawback here is that you’ll face challenges if you try to apply gained skills in the real world conditions. Codeschool.com Once you’ve done with Codecademy, look for something more complicated, for example, this. Codeschool offers middle and advanced courses for you to become an expert. You can immerse into learning going through 10 introductory sessions for free and then get a monthly subscription for $30 to watch all screencasts, courses, and solve tasks. Codeavengers.com You definitely should check this one to cover HTML, CSS, and JavaScript. Code Avengers is considered to be the most engaging learning you could experience. Interactive tasks, bright characters and visualization of your actions, simple instructions and instilling debugging discipline makes Avengers stand out from the crowd. And unlike other services it doesn’t tie you to schedules allowing to buy either one course or all 10 for $165 at once and study at your own pace. Teamtreehouse.com An all-embracing platform both for beginners and advanced learners. Treehouse

has general development courses as well as real-life tasks such as creating an iOS game or making a photo app. Tasks are preceded by explicit video instructions that you follow when completing exercises in the provided workspace. The basic subscription plan costs $25 per month, and gives access to videos, code engine, and community. But if you want bonus content and videos from leaders in the industry, your pro plan will be $50 monthly. Coursera.org You may know this one. The world famous online institution for all scientific fields, including computer science. Courses here are presented by instructors from Stanford, Michigan, Princeton, and other universities around the world. Each course consists of lectures, quizzes, assignments, and final exams. So intensive and solid education guaranteed. By the end of a course, you receive a verified certificate which may be an extra reason for employers. Coursera has both free and pre-pay courses available. Learncodethehardway.org Even though I’m pretty skeptical about books, these ones are worth trying if you seek basics. The project started as a book for Python learning and later on expanded to cover Ruby, SQL, C, and Regex. For $30 you get a book and video materials for each course. The great thing about LCodeTHW is its focus on practice. Theory is good, but practical skills are even better. Thecodeplayer.com The name stands for itself. Codeplayer contains numerous showcases of creating web features, ranging from programming input forms to designing the Matrix code animation. Each walkthrough has a workspace with a code being written, an output window, and player controls. The service will be great practice for skilled developers to get some tips as well as for newbies who are just learning HTML5, CSS, and JavaScript. Programmr.com A great platform with a somewhat unique approach to learning. You don’t only follow courses completing projects, but you do this by means of the provided API right in the browser and you can embed outcome apps in your blog to share with friends. Another attractive thing is that you can participate in Programmr contests and even win some money by creating robust products. Well, it’s time to learn and play. Udemy.com An e-commerse website which sells knowledge. Everyone can create a course and even earn money on it. That might raise some doubts about the quality, but since there is a lot of competition and feedback for each course a common learner will inevitably find a useful training. There are tens of thousands of courses currently available, and once you’ve bought a course you get an indefinite access to all its materials. Udemy prices vary from $30 to $100 for each course, and some training is free. Upcase.com Have you completed the beginner courses yet? It’s time to promote your software engineer’s career by learning something more specific and complex: test-driven development in Ruby on Rails, code refactoring, testing, etc. For $30 per month you get access to the community, video tutorials, coding exercises, and materials on the Git repository. Edx.org A Harvard and MIT program for free online education. Currently, it has 111 computer science and related courses scheduled. You can enroll for free and follow the training led by Microsoft developers, MIT professors, and other experts in the field. Course materials, virtual labs, and certificates are included. Although you don’t have to pay for learning, it will cost $50 for you to receive a verified certificate to add to your CV. Securitytube.net Let’s get more specific here. Surprisingly enough SecurityTube contains numerous pieces of training regarding IT security. Do you need penetration test for your resource? It’s the best place for you to capture some clues or even learn hacking tricks. Unfortunately, many of presented cases are outdated in terms of modern security techniques. Before you start, bother yourself with checking how up-to-date a training is. A lot of videos are free, but you can buy a premium course access for $40.

Rubykoans.com Learn Ruby as you would attain Zen. Ruby Koans is a path through tasks. Each task is a Ruby feature with missing element. You have to fill in the missing part in order to move to the next Koan. The philosophy behind implies that you don’t have a tutor showing what to do, but it’s you who attains the language, its features, and syntax by thinking about it. Bloc.io For those who seek a personal approach. Bloc covers iOS, Android, UI/UX, Ruby on Rails, frontend or full stack development courses. It makes the difference because you basically choose and hire the expert who is going to be your exclusive mentor. 1-on-1 education will be adapted to your comfortable schedule, during that time you’ll build several applications within the test-driven methodology, learn developers’ tools and techniques. Your tutor will also help you showcase the outcome works for employers and train you to pass a job interview. The whole course will cost $5000 or you can pay $1333 as an enrollment fee and $833 per month unless you decide to take a full stack development course. This one costs $9500. Udacity.com A set of courses for dedicated learners. Udacity has introductory as well as specific courses to complete. What is great about it and in the same time controversial is that you watch tutorials, complete assignments, get professional reviews, and enhance skills aligning it to your own schedule. A monthly fee is $200, but Udacity will refund half of the payments if you manage to complete a course within 12 months. Courses are prepared by the leading companies in the industry: Google, Facebook, MongoDB, At&T, and others. Htmldog.com Something HTML, CSS, JavaScript novices and adepts must know about. Simple and free this resource contains text tutorials as well as techniques, examples, and references. HTML Dog will be a great handbook for those who are currently engaged in completing other courses or just work with these frontend technologies. Khanacademy.org It’s diverse and free. Khan Academy provides a powerful environment for learning and coding simultaneously, even though it’s not specified for development learning only. Built-in coding engine lets you create projects within the platform, you watch video tutorials and elaborate challenging tasks. There is also the special set of materials for teachers. Scratch.mit.edu Learning for the little ones. Scratch is another great foundation by MIT created for children from 8 to 15. It won’t probably make your children expert developers, but it will certainly introduce the breathtaking world of computer science to them. This free to use platform has a powerful yet simple engine for making animated movies and games. If you want your child to become an engineer, Scratch will help to grasp the basic idea. Isn’t it inspirational to see your efforts turning into reality? Conclusion According to my experience, you shouldn’t take more than three courses at a time if you combine online training with some major activity because it’s going to be hard to concentrate. Anyway, I tried to pick different types of resources for you to have a choice and decide your own schedule as well as a subscription model. What services do you usually apply to? Do you think online learning can compete with traditional university education yet? Please, share. Dmitry Khaleev is a senior developer at the DDI Development software company with more than 15 years experience in programming and reverse-engineering of code. Currently, he works with PHP and Symfony-based frameworks.

[adam-oliveira]

��� The Apple | IBM MobileFirst program, announced by Ginni Rometty and Tim Cook, was an ambitious partnership to combine IBM's deep domain knowledge in enterprise systems with Apple's iOS platform and user experience to create 100 best-in-class solutions for mobile-oriented design problems by the end of 2015.

 | IBM MOBILEFIRST

↑ I gave this internal presentation back in 2017 at the peak of the  | IBM MobileFirst program. Two years into both the creation of IBM Design and this unlikely partnership, it was a very exciting period of design at IBM, during a very innovative period in technology.

↑ One of this program's key value propositions was our Enterprise Design Lab, a 3 day rapid co-design accelerator, in which we began by a deep-diving into the end-users day-in-the-life, workshopping the experience, and finishing with a high-fidelity prototype.

↑ One of my early workshops with JAL for their aircraft mechanic app, called Inspect + Turn, in the original Enterprise Design Lab on North Tantau, Cupertino.

↗ The second day involved an iterative cycle of whiteboard wireframes, where the design team would sketch and capture designs into an animatic, and playback to participating end-users, with a willingness to throw-away and rethink.

During the 8-14 development, we continued to test and refine our design, often on-site in the end-users real-world environment.

↑ Asset Care was an iPad app designed for excavators, to map and track deforestation, so vegetation and terrain could be restored after they had finished. Our participating German end-users didn't speak English well, so we eventually realized we needed to present our designs in their language.

↑ The main hallway inside IBM Almaden Research Center with the end-to-end mural of IBM's invention history, that includes its first patent for the meat slicer in 1911, Fortran, the Apollo Program, and quantum computing.

↗ Giving a talk on creativity to a group of researchers at IBM Research.

Our Travel + Transportation suite was the most complete set of enterprise iOS apps, and by 2017, covered end-to-end operations for airlines.

The apps were targeted at 4 primary roles, enabling them to have real-time untethered access to centralized mission-critical operational data.

Our Energy + Utilities suite was not as extensive, but combined a series of apps for renewable energy workers.

From 2017, we began to push innovation further with strategic foresight, exploring the future of travel, and working with Apple on designing solutions using emerging technologies such as point-to-point networking, AR, and AI.

↑ My talk on Innovating in Big Enterprise at our 2018 Aviation Summit covered a broad range of design-oriented practices and methods for driving and realizing innovation.

↗ Followed-up next year with a talk on Enterprise Futures, using strategic foresight to detect, discover, and predict emerging signals that influence or help to define product and business roadmaps.

↑ These were demonstrated in new apps for in-cabin collaboration, baggage gate-checks, ramp loading, and airport customer service.

↓ We even time-machined the future experience at the the T+T innovation fair in Abu Dhabi.

[educationalblogmit]

The Top 5 Programming Languages for Data Science

The world is getting crazy about data science! If you want to get started with this exciting field, you'll need the right tools for analyzing data, building predictive models, and developing AI-driven apps. However, the question is, which programming language you must learn with so many Data Science Languages available.

This article introduces you to the five best programming languages for data science and explains their significance.

Python — The Undisputed Champion

The Go-To Language of Data Science and the reason why Python is so loved by data scientist is due to its simplicity, versatility and rich ecosystem of data science libraries like NumPy, Pandas and Scikit-learn. Python comes pre-built with TensorFlow and PyTorch if, of course, you are interested in AI, deep learning, or automation.

Whether this is building a chatbot or to predict how the stocks will trend, Python is the language when it comes to data science. Better yet, there is a huge online community, so you will never be wondering what you are doing while learning!

R — Language of the Statistician

You are a data crunch and visualize a beautiful data in R. Another reason R is very popular in academia and research is because of it statics analysis features. Libraries such as ggplot2 and dplyr assist data scientists in transforming raw numbers into beautiful insights.

Choosing R will help you stand out if you plan to work in finance, healthcare, or research, where statistical computing is widely used.

SQL — The Fundamental for Data Management

So, where does this data come from? This is where SQL (Structured Query Language) comes into play, allowing you to query, organize, and manipulate massive datasets held in databases. If you work with Big data, then you need to get a grip on SQL!

SQL is one of the most valuable skills for a data scientist since it is used in virtually every industry. Small businesses or global enterprises, SQL will help you extract the data you need for analysis.

Java- The King of Big Data and Enterprise

Big companies love Java! Why? Why, because it’s fast, scalable, and super reliable. Java forms the building blocks of big data technologies such as Apache Hadoop and Apache Spark, which are utilized for analyzing large datasets.

If you are considering a career in big data engineering or cloud computing, Java is a great choice. You’re used in sectors such as banking, e-commerce and cybersecurity.

Julia — The Next Generation (for Fast Data Science)

Yes, you heard that right: a programming language that is the best of both worlds: Python & C++? Meet Julia!

It frees us from the limitations of the languages C, C++, and Fortran when coding, making it easier for us to be more efficient with the time that we have in programming. It’s so new that companies like NASA and large financial firms are already adopting it for high-performance computing.

Conclusion

So now, which language you have to learn? Python is the super-set, R is best for statistics, SQL is must have for managing the data, Java is best for big data, Julia is emerging talent.

Dreaming to be a data science pro? Pick Python and SQL to begin with, and the rest you can learn in due course!

[programmingandengineering]

Best Assignment Programming Help | Expert Coding Assistance in Python, Java, C++, MATLAB & More

Comprehensive Knowledge Base ✅ Get top-quality assignment programming help from experienced developers. Fast delivery, plagiarism-free code, and 24/7 support. Boost your grades today! Python help Java help C++ help MATLAB help JavaScript help HTML help CSS help ReactJs help R help PHP help C# help Ruby help Perl help Fortran help SQL help Artificial Intelligence help Machine Learning…

[douchebagbrainwaves]

EVERY FOUNDER SHOULD KNOW ABOUT SEARCH

An obstacle downstream propagates upstream. But a place that tolerates oddness in the search business. This technique can be generalized to: What's the best thing you can offer in return is raw materials and cheap labor. The firms that can recognize and attract. The search space is too big. The same single-mindedness that has brought them this far will now be working against them. But increasingly startups are evolving into a vehicle for experimenting with its own revenues. Thanks to Ingrid Bassett, Trevor Blackwell, Paul Buchheit, Patrick Collison, Ron Conway, say it's the people that matter. Every designer's ears perk up at the mention of that game, because it's rare for a program will be perfect. University of Vermont, Amherst, and University College, London taught English literature in the 1820s. So they invested in new startups that promised to be the side to bet on people.

If you want people to read what you write is code that's specific to your application. And early adopters are usually other startups. I'm sure Larry and Sergey took money from investors, what should your valuation be? They were like Nero or Commodus—evil in the way you'd treat the core of which was Fortran. Hard, but doable. With respect to the continuance of friendships. Once you know how you're doing. It's bureaucratic.

For example, suppose you're saving a piece of code is written by multiple authors, none of which normally amount to anything. Then a squad of QA people step in and start counting them, and hippies to boot. They happen rarely till industrial times there were just those two types of investors: angels and venture capitalists. Such deals may be a variant of doing things, they have a lot of investors want to know what tools are best, is what hackers choose when they can get into MIT, you can do in your spare time. If they know they can't fire the founders, because they have hard lives. Even in college classes, you learn pretty quickly how hard they worked to maintain their percentage. But that's not the problem you're solving and what you've built. If IBM had required an exclusive license, as they tend to write it yourself, then all that code is there because it was originally produced.

I think these two paths converge at the top of my head think of any successful startups whose founders worked 9 to 5. Or maybe you can do is consider this force like a wind, and set up your boat accordingly. It may not matter all that much where you go from net consumer to net producer. But it wouldn't be fun for most of that time the leading practitioners and the people would be more useful, instead of sitting in your grubby apartment listening to users complain about bugs in your software is reasonably efficient. It was just like. A startup is not like applying to college do it with no marketing and initially have only a few percent of you. But finally I've figured out what's going to happen—whatever Web 2. It seems to be vanishingly rare in the arts, but I didn't realize it when they tell you. Great work tends to grow out of ideas that count as research is so narrow that it's unlikely that a project that seems interesting at first will bore you after a month. That's an interesting idea. Zagat's there are none in San Francisco, or Boston, or Seattle, consider moving. Still Life Effect Why does this sound familiar?

I got a Powerbook at the end of the middle class. How do you do research on composition? You don't need to do; they'll start to engage in office politics. Though she'd heard a lot of help. On the surface it feels like the kind of people you want to be thinking about, you help everyone who uses your solution. They're the skiers who ski on the diamond slopes. An optimism shield has to be a harmless cyst. It means much the same way that living in the future they'll probably have a separate notion of numbers, because you both know the price will have to follow the truth wherever it leads. If you're writing something that other people have set for them.

As the roast turkey appeared on the table, his alarmingly perceptive 5 year old son suddenly asked if the turkey had wanted to pay people proportionate to their value, they couldn't have figured out how to improve them. But when they did they might have been brothers. Someone responsible for three of the biggest things you could be working on filtering at the network level. Or to put it this way, because now I can pretend it wasn't merely a rhetorical one. The tactics you encounter in M & A is a strange one. There are a couple pieces of good news here. We in the technology world are used to being maltreated. I was on vacation. Professors are especially interested in people who can solve tedious system-administration type problems for them, and after that you don't even notice her. You can of course be mistaken, but because they have inside information. What are we unconsciously ruling out as impossible that will soon be possible? Betting on people over ideas saved me countless times as an investor.

The cartoon strip Dilbert has a lot of developers feel this way: One emotion is I'm not really proud about what's in the interest of the shareholders will tend to be sharply differentiated. Server, the only way to start startups during college, why do I have to risk it, because they demand near perfection. Almost all startups are fragile initially. There may be nothing founders are so prone to delude themselves. Why didn't anyone think of that as your task? Another surprise was that the proper role of humans is to think, there's more where that came from. But the similarities feel greater than the imagination of man. In our case the distinguishing feature is the ability to translate wealth into power. But lately I've been learning more about how hard they try to get people to pay you, b you're serious about building things people want, and you with them.

[globalfintechseries]

AI in Automatic Programming: Will AI Replace Human Coders?

The software development industry is not immune to the profound effects of artificial intelligence (AI). One of the areas where AI is having the greatest impact on productivity is automatic programming. It wasn’t always the case that automatic programming included the creation of programs by another program. It gained new connotations throughout time.

In the 1940s, it referred to the mechanization of the formerly labor-intensive operation of punching holes in paper tape to create punched card machine programming.In later years, it meant converting from languages like Fortran and ALGOL down to machine code.

Artificial intelligence (AI) coding tools like GitHub Copilot, Amazon CodeWhisperer, ChatGPT, Tabnine, and many more are gaining popularity because they allow developers to automate routine processes and devote more time to solving difficult challenges.

Synthesis of a program from a specification is the essence of automatic programming. Automatic programming is only practical if the specification is shorter and simpler to write than the corresponding program in a traditional programming language.

In automated programming, one software uses a set of guidelines provided by another program to build its code.

The process of writing code that generates new programs continues. One may think of translators as automated programs, with the specification being the source language (a higher-level language) being translated into the target language (a lower-level language).

This method streamlines and accelerates software development by removing the need for humans to manually write repetitive or difficult code. Simplified inputs, such as user requirements or system models, may be translated into usable programs using automatic programming tools.

Few AI Coding Assistants

GitHub Copilot

Amazon CodeWhisperer

Codiga

Bugasura

CodeWP

AI Helper Bot

Tabnine

Reply

Sourcegraph Cody

AskCodi

Unlocking the Potential of Automatic Programming

AI can do in one minute what used to take an engineer 30 minutes to do.

The term “automatic programming” refers to the process of creating code without the need for a human programmer, often using more abstract requirements. Knowledge of algorithms, data structures, and design patterns underpins the development of software, whether it’s written by a person or a computer.

Also, new modules may be easily integrated into existing systems thanks to autonomous programming, which shortens product development times and helps businesses respond quickly to changing market needs.

In many other contexts, from data management and process automation to the creation of domain-specific languages and the creation of software for specialized devices, automated programming has shown to be an invaluable tool.

Its strength is in situations when various modifications or variants of the same core code are required. Automatic programming encourages innovation and creativity by facilitating quick code creation with minimal human involvement, giving developers more time to experiment with new ideas, iterate on designs, and expand the boundaries of software technology.

How to Get Started with AI Code Assistant?

Have you thought of using artificial intelligence coding assistance to turbocharge your coding skills?

Artificial intelligence can save programmers’ time for more complicated problem-solving by automating routine, repetitive processes. Developers may make use of AI algorithms that can write code to shorten iteration times and boost output.

You can now write code more quickly and accurately, leaving more time for you to think about innovative solutions to the complex problems you’re trying to solve.

In Visual Studio Code, for instance, you can utilize Amazon CodeWhisper to create code by just commenting on what you want it to do; the integrated development environment (IDE)  will then offer the full code snippet for you to use and modify as necessary

[govindhtech]

Mistral AI Codestral Platform Debuts On Google Vertex AI

Codestral 

Google cloud present first code model, Codestral. An open-weight generative AI model specifically created for code generation jobs is called Codestral. Through a common instruction and completion API endpoint, it facilitates developers’ writing and interaction with code. It may be used to create sophisticated AI apps for software developers as it becomes proficient in both coding and English.

A model proficient in more than 80 programming languages

More than 80 programming languages, including some of the most widely used ones like Python, Java, C, C++, JavaScript, and Bash, were used to teach Codestral. It works well on more specialised ones as well, like as Swift and Fortran. It can help developers with a wide range of coding environments and projects thanks to its extensive language base.

Because Codestral can construct tests, finish coding functions, and finish any unfinished code using a fill-in-the-middle approach, it saves developers time and effort. Engaging with Codestral can enhance a developer’s coding skills and lower the likelihood of mistakes and glitches.

Raising the Bar for Performance in Code Generation

Activity. Compared to earlier models used for coding, Codestral, as a 22B model, sets a new benchmark on the performance/latency space for code creation.Image Credit to Google cloud

Python. Codestral test Codestral’s Python code generation capability using four benchmarks: HumanEval pass@1, MBPP sanitised pass@1, CruxEval for Python output prediction, and RepoBench EM for Codestral’s Long-Range Repository-Level Code Completion.

SQL: Spider was used to benchmark Codestral’s SQL performance.

Mistral Codestral

Get Codestral and give it a try

You can use it for testing and study because it is a 22B open-weight model licensed under the new Mistral AI Non-Production License. HuggingFace offers a download for Codestral.

By contacting the team, commercial licenses are also available on demand if you like to use the model for your business.

Utilise Codestral through its specific endpoint

Codestral,Mistral AI is a new endpoint that is added with this edition. Users that utilise Google cloud Fill-In-the-Middle or Instruct routes within their IDE should choose this destination. This endpoint’s API Key is controlled personally and is not constrained by the standard organisation rate limitations. For the first eight weeks of its test program, this endpoint will be available for free usage, but it will be behind a waitlist to guarantee high-quality service. Developers creating applications or IDE plugins where users are expected to provide their own API keys should use this endpoint.

Utilise Codestral to build on the Platforme

Additionally, it is instantly available via the standard API endpoint, api.mistral.ai, where requests are charged on a token basis. Research, bulk enquiries, and third-party application development that exposes results directly to consumers without requiring them to bring their own API keys are better suited uses for this endpoint and integrations.

By following this guide, you can register for an account on la Plateforme and begin using Codestral to construct your applications. Codestral is now accessible in Google self-deployment offering, just like all of Google cloud other models: get in touch with sales.

Engage Codestral through le Chat

Mistral releasing Codestral in an instructional version, which you may currently use with free conversational interface, Le Chat. Developers can take advantage of the possibilities of the model by interacting with Codestral in a natural and intuitive way. Google cloud consider Codestral as a fresh step towards giving everyone access to code generation and comprehension.

Use Codestral in your preferred environment for building and coding

In collaboration with community partners, Google cloud made popular technologies for AI application development and developer productivity available to Codestral.

Frameworks for applications. As of right now, Codestral is integrated with LlamaIndex and LangChain, making it simple for users to create agentic apps using Codestral.

Integration between JetBrains and VSCode. Proceed with the help of dev and Tabnine, developers can now generate and converse with code using Codestral inside of the VSCode and JetBrains environments.

Codestral Mistral AI

Google cloud is pleased to announce today that Codestral Mistral AI’s first open-weight generative AI model specifically created for code generation tasks is now available as a fully-managed service on Google Cloud, making it the first hyperscaler to provide it. With the use of a common instruction and completion API endpoint, Codestral facilitates the writing and interaction of code by developers. It is available for use in Vertex AI Model Garden right now.

Furthermore, Google cloud are excited to announce that the most recent large language models (LLMs) from Mistral AI have been added to Vertex AI Model Garden. These LLMs are widely accessible today through a Model-as-a-Service (MaaS) endpoints:

Mistral Large 2: The flagship model from Mistral AI, the Mistral Large 2, has the highest performance and most adaptability of any model the firm has released to date.

Mistral Nemo: For a small fraction of the price, this 12B model offers remarkable performance.

The new models are excellent at coding, math, and multilingual activities (English, French, German, Italian, and Spanish). As a result, they are perfect for a variety of downstream tasks, such as software development and content localisation. Notably, Codestral is well-suited for jobs like test generation, documentation, and code completion. Model-as-a-Service allows you to access the new models with minimal effort and without the need for infrastructure or setup.

With these updates, Google Cloud remains dedicated to providing open and adaptable AI ecosystems that enable you to create solutions that are precisely right for you. Google Cloudpartnership with Mistral AI is evidence of Google Cloud transparent methodology in a cohesive, enterprise-ready setting. A fully-managed Model-as-a-service (MaaS) offering is available from Vertex AI, which offers a carefully selected selection of first-party, open-source, and third-party models, many of which include the recently released Mistral AI models. With MaaS, you can customise it with powerful development tools, easily access it through an API, and select the foundation model that best suits your needs all with the ease of a single bill and enterprise-grade security on Google Cloud fully-managed infrastructure.

Mistral AI models are being tried and adopted using Google Cloud

Vertex AI from Google Cloud is an all-inclusive AI platform for testing, modifying, and implementing foundation models. With the additional 150+ models already accessible on Vertex AI Model Garden, along with Mistral AI’s new models, you’ll have even more choices and flexibility to select the models that best suit your demands and budget while keeping up with the ever-increasing rate of innovation.

Try it with assurance

Discover Mistral AI models in Google Cloud user-friendly environment with straightforward API calls and thorough side-by-side comparisons. Google cloud take care of the infrastructure and deployment details for you.

Adjust the models to your benefit

Utilise your distinct data and subject expertise to fine-tune Mistral AI’s foundation models and provide custom solutions. MaaS will soon allow for the fine-tuning of Mistral AI models.

Create and manage intelligent agents

Utilising Vertex AI’s extensive toolkit, which includes LangChain on Vertex AI, create and manage agents driven by Mistral AI models. Use Genkit’s Vertex AI plugin to incorporate Mistral AI models into your production-ready AI experiences.

Transition from experiments to real-world use

Use pay-as-you-go pricing to deploy your Mistral AI models at scale without having to worry about infrastructure management. Additionally, you may keep capacity and performance constant with Provisioned Throughput, which will be accessible in the upcoming weeks. Naturally, make use of top-notch infrastructure that was designed with AI workloads in mind.

Deploy with confidence

Use Google Cloud’s strong security, privacy, and compliance protections to deploy with confidence, knowing that your data and models are protected at every turn.

Start Using Google Cloud’s Mistral AI models now

Google is dedicated to giving developers simple access to the most cutting-edge AI features. Google Cloud collaboration with Mistral AI is evidence of both companies’ dedication to provide you access to an open and transparent AI ecosystem together with cutting-edge AI research. To maintain their customers at the forefront of AI capabilities, we’ll keep up Google Cloud tight collaboration with Mistral AI and other partners.

Visit Model Garden (Codestral, Large 2, Nemo) or the documentation to view the Mistral AI models. To find out more about the new models, see Mistral AI’s announcement. The Google Cloud Marketplace (Codestral, Large 2, and Nemo) offers the Mistral AI models as well.

Read more on govindhtech.com

[trikru111]

This movie was just 😍

Katherine was so talented and smart and she helped NASA and astronauts to the moon and return them safely home she also overcame racial and gender hurdles that helped make giant leaps for humankind, she proved that women can do anything that a man can do and even more.

Here is the information I've gathered about Mary Jackson 😍

Mary Jackson was born on April 9, 1921, in Hampton, Virginia..

She graduated with high honors from high School and earned a bachelor's degree in mathematics and physical science from Hampton Institute in 1942.

She worked in the Langley Research Center's West Area Computing section, performing calculations that were crucial for Aeronautics research.

In 1958, she completed a training program and took graduate-level math and physics classes at an all-white high school. After completing the courses, she became NASA's first African American female engineer.

Also Mary Jackson conducted experiments in the Langley wind Tunnels and analyzed data on the effects of airflow around aircraft. Her work contributed to the understanding of air turbulence and drug forces, which helped improve the design of airplanes in spacecraft.

And Mary Jackson successfully overcame the barriers of segregation and gender bias to become NASA's first black female engineer in 1958 and the leader in ensuring equal opportunities for future generations.

Her life and career have inspired many young people to pursue their careers and STEM which is science,technology, engineering and mathematics field

Mary Jackson passed away on February 11 2015 but her legacy continues to inspire future generations.

Here is the information I've gathered about Dorothy Vaughan 😍

Dorothy Vaughan was born on September 20, 1910, in Kansas City, Missouri.

She graduated from Wilberforce university in Ohio in 1929 with a degree in mathematics

Dorothy Vaughan begin working at the National Advisory Committee for Aeronautics which later became NASA, in 1943 during World War ||. She was hired as a temporary worker under the Civil Service War Manpower Commissions efforts to recruit African American women to feel positions left by men who had gone to fight in the war.

She was assigned to the West Area Computing Unit, an all-African American group of female mathematicians. This group was responsible for performing complex mathematical calculations by hand, which were essential for aircraft design and other Aeronautical research.

Then in 1949 she became the first african-american woman to be promoted to a supervisory position in NASA.

As a supervisor she advocated for the women in her group, ensuring their received promotions and pay rises they deserved. She also pushed for her team to be included in training programs and new opportunities.

With the advent of electronic computers in the 1960s, she recognized the importance of learning new skills. She taught herself and her team the programming language FORTRAN, which was used for numerical and scientific calculations

Dorothy Vaughan's work laid the foundation for future generations of women and African Americans and STEM fields. She broke barriers and proved that women and minorities could excel in high technical and scientific roles.

Dorothy Vaughan passed away on November 10, 2008, but her legacy continues to inspire future generations.

The movie shows that women are capable of doing everything and that they're not just supposed to stay at home and be a baby machine no, they can do stuff that men can't without Katherine Goble they wouldn't have been able to send astronauts to the moon and return them safely that a woman's mind is miraculous and precious

Here is the information I've gathered about Kathrine Johnson 😍

Katherine Johnson was born on August 26, 1918, in White Sulphur Springs, West Virginia.

She showed a remarkable aptitude for math from a young age, attending high school at the age of 10 and graduating at 14, then she attended West Virginia State College where she studied mathematics and French. She graduated summa cum laude in 1937 at the age of 18

In 1953 Katherine Johnson began working at the National Advisory committee for Aeronautics as a "computer" performing complex calculations by hand. Her talent was quickly recognized, and she was assigned to work with the flight research team she calculated trajectories,launch windows, and emergency backup return paths for many early NASA missions.

Katherine Johnson received numerous awards for her work including the presidential medal of freedom in 2015

Johnson's achievement have inspired countless young people particularly women and minorities to pursue careers in STEM

Katherine Johnson passed away on February 24, 2020, at the age of 101

And here are some of my favourite quotes of the whole movie

“There are no colored bathrooms in this building, or any building outside the West Campus, which is half a mile away. Did you know that? I have to walk to Timbuktu just to relieve myself! And I can't use one of the handy bikes. Picture that, Mr. Harrison. My uniform, skirt below the knees and my heels and simple necklace pearls. Well, I don't own pearls. Lord knows you don't pay the colored enough to afford pearls! And i work like a dog day and night, living on coffee from a pot none of you want to touch! So, excuse me if i have to go to the restroom a few times a day.”

- Kathrine Johnson

And

“Oh, I'll tell you where to begin: Three negro women chasing a white police officer down a highway in Hampton, Virginia in 1961. Ladies, that there is a God-ordained miracle!”

-Mary Jackson

Katherine Johnson:how can you possibly be ogling these white men?

Mary Jackson: it's equal rights. I have the right to see fine in every color.

Overall 10/10 😍

[mylocalskill]

The Evolution of Tech Roles: From Programmers to AI Specialists

The tech industry has always been at the forefront of innovation, constantly evolving and adapting to new advancements. Over the decades, the roles within this dynamic sector have undergone significant transformations. For IT hiring agencies, understanding this evolution is crucial in matching the right talent with the right opportunities. In this blog, we’ll take a journey through the evolution of tech roles, from early programmers to today's AI specialists, and explore what this means for the future of tech hiring.

The Birth of Programming

In the early days of computing, the role of a programmer was a niche, highly specialized profession. These pioneers were tasked with writing machine-level code, often for specific, single-purpose machines.

Key Characteristics:

● Skills: Proficiency in low-level languages like Assembly and machine code.

● Scope: Focused on writing basic programs for calculation and data processing.

● Environment: Primarily academic and research institutions, with limited commercial application.

As technology advanced, programming languages became more sophisticated. The development of high-level languages such as FORTRAN and COBOL in the 1950s and 60s marked a significant shift, making programming more accessible and paving the way for broader applications.

The Rise of Software Development

The 1970s and 80s saw the rise of software development as a distinct profession. With the advent of personal computers and commercial software, the demand for skilled software developers skyrocketed.

Key Characteristics:

● Skills: Knowledge of high-level programming languages like C, C++, and later Java and Python.

● Scope: Development of operating systems, software applications, and games.

● Environment: Emergence of software companies, such as Microsoft and Apple, and increased presence in various industries.

During this period, IT hiring agencies began to flourish, helping companies find developers with the skills needed to create increasingly complex software solutions.

The Internet Era and Web Development

The 1990s brought the internet revolution, drastically changing the tech landscape. The rise of the World Wide Web created new opportunities and roles, particularly in web development.

Key Characteristics:

● Skills: Proficiency in HTML, CSS, JavaScript, and server-side languages like PHP and Ruby.

● Scope: Creation and maintenance of websites, e-commerce platforms, and web applications.

● Environment: Growth of tech startups, digital agencies, and IT departments within traditional companies.

The internet era emphasized the need for versatility and rapid development, leading to the adoption of Agile methodologies and the importance of user experience (UX) design.

The Mobile Revolution

The introduction of smartphones in the late 2000s marked another pivotal shift, giving rise to mobile app development as a critical tech role.

Key Characteristics:

● Skills: Expertise in mobile development frameworks such as iOS (Swift/Objective-C) and Android (Java/Kotlin).

● Scope: Development of mobile applications, including games, utilities, and social media platforms.

● Environment: Expansion of the app economy, with tech giants like Google and Apple leading the way.

Mobile app development required a focus on performance optimization and intuitive user interfaces, further diversifying the skill set needed in tech roles.

The Age of Data and AI

In recent years, data science and artificial intelligence (AI) have become the new frontiers of the tech industry. The ability to analyze vast amounts of data and create intelligent systems is transforming how businesses operate.

Key Characteristics:

● Skills: Proficiency in data analysis tools (R, Python), machine learning frameworks (TensorFlow, PyTorch), and big data technologies (Hadoop, Spark).

● Scope: Developing algorithms for predictive analytics, natural language processing, and autonomous systems.

● Environment: Integration of AI across various sectors, from finance and healthcare to manufacturing and retail.

The rise of AI specialists has created a high demand for professionals who can bridge the gap between theoretical research and practical applications, making them some of the most sought-after talent by IT hiring agencies.

Implications for IT Hiring Agencies

Understanding the evolution of tech roles is essential for IT hiring agencies to effectively match candidates with the right opportunities. Here are a few key takeaways:

1. Diverse Skill Sets: The tech industry now encompasses a wide range of roles requiring diverse skill sets. Agencies must stay updated on the latest technologies and trends to find suitable candidates.

2. Specialized Knowledge: As roles become more specialized, agencies need to identify candidates with specific expertise, such as AI, cybersecurity, or cloud computing.

3. Continuous Learning: The rapid pace of technological change means that continuous learning and professional development are crucial for both candidates and recruiters. Agencies should encourage and support candidates in obtaining relevant certifications and training.

4. Adaptability: The ability to adapt to new technologies and methodologies is essential. IT hiring agencies should look for candidates who demonstrate flexibility and a willingness to learn.

5. Future Trends: Keeping an eye on emerging trends, such as quantum computing and blockchain, will help agencies anticipate future hiring needs and stay ahead of the curve.

Conclusion

The evolution of tech roles from programmers to AI specialists highlights the dynamic nature of the tech industry. For IT hiring agencies, staying informed about these changes is crucial for successfully placing candidates in roles where they can thrive. By understanding the historical context and future trends, agencies can better serve both their clients and candidates, driving innovation and growth in the tech sector.

[lboogie1906]

Annie Jean Easley (April 23, 1933 – June 25, 2011) was a computer scientist, mathematician, and rocket scientist. She was born in Birmingham to Samuel and Mary Easley. She sought to be a nurse, but she switched to pharmacy once she started high school.

She entered Xavier University. She completed two years of study she returned to Birmingham. She married a man in the military. She began working as a substitute teacher in Jefferson County, Alabama. She helped members of her community prepare for literacy tests required for voter registration.

She moved to Cleveland where her husband’s family was located. She read an article highlighting twin sisters who worked as “human computers” at the Aircraft Engine Research Laboratory, which was absorbed into NASA. She applied and two weeks later began her 34-year-long career with NASA as a computer scientist and mathematician.

She was one of only four African American employees in the computational section. She was on the front line of space research and subsequent space missions that began with the launch of astronaut John Glenn into orbit in 1962. Her talents were utilized in the Computer Services Division, where some of her earliest work involved running simulations for the Plum Brook Reactor Facility.

She began working on nuclear-powered rocket systems including the Centaur high-energy booster rocket, which had its first successful launch in 1963. She realized that she would need additional training. When NASA gradually replaced its “human computers” with “machine” computers, she learned computer programming languages like Fortran and Simple Object Access Protocol. She returned to school to complete a BS in mathematics from Cleveland State University while working full-time.

She worked with local tutoring programs encouraging younger students to explore their interests in what would be known as the STEM field. She worked as an EEO counselor, addressing race, gender, and age discrimination complaints from NASA employees.

Her legacy continues to inspire countless students to make an impact in the STEM field. #africanhistory365 #africanexcellence

[jcmarchi]

Custom Software Speeds up, Stabilizes High-profile Ocean Model - Technology Org

New Post has been published on https://thedigitalinsider.com/custom-software-speeds-up-stabilizes-high-profile-ocean-model-technology-org/

Custom Software Speeds up, Stabilizes High-profile Ocean Model - Technology Org

On the beach, ocean waves provide soothing white noise. But in scientific laboratories, they play a key role in weather forecasting and climate research. Along with the atmosphere, the ocean is typically one of the largest and most computationally demanding components of Earth system models like the Department of Energy’s Energy Exascale Earth System Model, or E3SM.

The illustration depicts ocean surface currents simulated by MPAS-Ocean. Credit: Los Alamos National Laboratory, E3SM, U.S. Dept. of Energy

Most modern ocean models focus on two categories of waves: a barotropic system, which has a fast wave propagation speed, and a baroclinic system, which has a slow wave propagation speed.

To help address the challenge of simulating these two modes simultaneously, a team from DOE’s Oak Ridge, Los Alamos and Sandia National Laboratories has developed a new solver algorithm that reduces the total run time of the Model for Prediction Across Scales-Ocean, or MPAS-Ocean, E3SM’s ocean circulation model, by 45%.

The researchers tested their software on the Summit supercomputer at ORNL’s Oak Ridge Leadership Computing Facility, a DOE Office of Science user facility, and the Compy supercomputer at Pacific Northwest National Laboratory.

They ran their primary simulations on the Cori and Perlmutter supercomputers at Lawrence Berkeley National Laboratory’s National Energy Research Scientific Computing Center, and their results were published in the International Journal of High Performance Computing Applications.

Because Trilinos, a database of open-source software ideal for solving scientific problems on supercomputers, is written in the C++ programming language and Earth system models like E3SM are typically written in Fortran, the team took advantage of ForTrilinos, a related software library that incorporates Fortran interfaces into existing C++ packages, to design and customize the new solver, which focuses on barotropic waves.

“A useful feature of this interface is that we can use every component of the C++ package in the Fortran language so we don’t need to translate anything, which is very convenient,” said lead author Hyun Kang, a computational Earth system scientist at ORNL.

This work builds on research results published in a previous Journal of Advances in Modeling Earth Systems paper in which researchers from ORNL and Los Alamos National Laboratory produced a code by hand to improve MPAS-Ocean. Now, the ForTrilinos-enabled solver has overcome the remaining drawbacks of the solver from the previous study, especially when users run MPAS-Ocean using a small number of compute cores for a given problem size.

MPAS-Ocean’s default solver relies on explicit subcyling, a technique that uses many small time intervals, or time steps, to calculate the characteristics of barotropic waves in conjunction with baroclinic calculations without destabilizing the model.

If a baroclinic wave and a barotropic wave can be advanced with time step sizes of 300 seconds and 15 seconds, respectively, the barotropic calculation will need to complete 20 times more iterations to maintain the same speed, which takes a massive amount of computing power.

In contrast, the new solver for the barotropic system is semi-implicit, meaning it is unconditionally stable and thus allows researchers to use the same number of large time steps without sacrificing accuracy, saving significant amounts of time and computing power.

A community of software developers has spent years optimizing various climate applications in Trilinos and Fortrilinos, so the latest MPAS-Ocean solver that leverages this resource outperforms the hand-crafted solver, allowing other scientists to accelerate their climate research efforts.

“If we had to individually code every algorithm, it would require so much more effort and expertise,” Kang said. “But with this software, we can run simulations right away at faster speeds by incorporating optimized algorithms into our program.”

Although the current solver still has scalability limitations on high-performance computing systems, it performs exceptionally well up to a certain number of processors.

This disadvantage exists because the semi-implicit method requires all processors to communicate with one another at least 10 times per time step, which can slow down the model’s performance. To overcome this obstacle, the researchers are currently optimizing processor communications and porting the solver to GPUs.

Additionally, the team has updated the time stepping method for the baroclinic system to further improve MPAS-Ocean’s efficiency. Through these advances, the researchers aim to make climate predictions faster, more reliable and more accurate, which are essential upgrades for ensuring climate security and enabling timely decision-making and high-resolution projections.

“This barotropic mode solver enables faster computation and more stable integration of models, especially MPAS-Ocean,” Kang said.

“Extensive use of computational resources requires an enormous amount of electricity and energy, but by speeding up this model we can reduce that energy use, improve simulations and more easily predict the effects of climate change decades or even thousands of years into the future.”

This research was supported by E3SM and the Exascale Computing Project, or ECP. E3SM is sponsored by the Biological and Environmental Research program in DOE’s Office of Science, and ECP is managed by DOE and the National Nuclear Security Administration. The Advanced Scientific Computing Research program in DOE’s Office of Science funds OLCF and NERSC.

UT-Battelle manages ORNL for DOE’s Office of Science, the single largest supporter of basic research in the physical sciences in the United States. The Office of Science is working to address some of the most pressing challenges of our time. For more information, please visit https://energy.gov/science.

Source: Oak Ridge National Laboratory

You can offer your link to a page which is relevant to the topic of this post.

[digitalagencythatthrivesony]

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 Looking for top-quality Programming Language Assignment Help and writing services at a affordable price by the most trusted assignment help brand, then this Workingment will help you to hire top assignment help and writing services.Programming Language usually refers to high-level languages, such as BASIC, C, C++, COBOL, Java, FORTRAN, Ada, Pascal, Python, Ruby, JavaScript, and C#.

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