#fortran tutorial

[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."

[govindhtech]

Intel VTune Profiler For Data Parallel Python Applications

Intel VTune Profiler tutorial

This brief tutorial will show you how to use Intel VTune Profiler to profile the performance of a Python application using the NumPy and Numba example applications.

Analysing Performance in Applications and Systems

For HPC, cloud, IoT, media, storage, and other applications, Intel VTune Profiler optimises system performance, application performance, and system configuration.

Optimise the performance of the entire application not just the accelerated part using the CPU, GPU, and FPGA.

Profile SYCL, C, C++, C#, Fortran, OpenCL code, Python, Google Go, Java,.NET, Assembly, or any combination of languages can be multilingual.

Application or System: Obtain detailed results mapped to source code or coarse-grained system data for a longer time period.

Power: Maximise efficiency without resorting to thermal or power-related throttling.

VTune platform profiler

It has following Features.

Optimisation of Algorithms

Find your code’s “hot spots,” or the sections that take the longest.

Use Flame Graph to see hot code routes and the amount of time spent in each function and with its callees.

Bottlenecks in Microarchitecture and Memory

Use microarchitecture exploration analysis to pinpoint the major hardware problems affecting your application’s performance.

Identify memory-access-related concerns, such as cache misses and difficulty with high bandwidth.

Inductors and XPUs

Improve data transfers and GPU offload schema for SYCL, OpenCL, Microsoft DirectX, or OpenMP offload code. Determine which GPU kernels take the longest to optimise further.

Examine GPU-bound programs for inefficient kernel algorithms or microarchitectural restrictions that may be causing performance problems.

Examine FPGA utilisation and the interactions between CPU and FPGA.

Technical summary: Determine the most time-consuming operations that are executing on the neural processing unit (NPU) and learn how much data is exchanged between the NPU and DDR memory.

In parallelism

Check the threading efficiency of the code. Determine which threading problems are affecting performance.

Examine compute-intensive or throughput HPC programs to determine how well they utilise memory, vectorisation, and the CPU.

Interface and Platform

Find the points in I/O-intensive applications where performance is stalled. Examine the hardware’s ability to handle I/O traffic produced by integrated accelerators or external PCIe devices.

Use System Overview to get a detailed overview of short-term workloads.

Multiple Nodes

Describe the performance characteristics of workloads involving OpenMP and large-scale message passing interfaces (MPI).

Determine any scalability problems and receive suggestions for a thorough investigation.

Intel VTune Profiler

To improve Python performance while using Intel systems, install and utilise the Intel Distribution for Python and Data Parallel Extensions for Python with your applications.

Configure your Python-using VTune Profiler setup.

To find performance issues and areas for improvement, profile three distinct Python application implementations. The pairwise distance calculation algorithm commonly used in machine learning and data analytics will be demonstrated in this article using the NumPy example.

The following packages are used by the three distinct implementations.

Numpy Optimised for Intel

NumPy’s Data Parallel Extension

Extensions for Numba on GPU with Data Parallelism

Python’s NumPy and Data Parallel Extension

By providing optimised heterogeneous computing, Intel Distribution for Python and Intel Data Parallel Extension for Python offer a fantastic and straightforward approach to develop high-performance machine learning (ML) and scientific applications.

Added to the Python Intel Distribution is:

Scalability on PCs, powerful servers, and laptops utilising every CPU core available.

Assistance with the most recent Intel CPU instruction sets.

Accelerating core numerical and machine learning packages with libraries such as the Intel oneAPI Math Kernel Library (oneMKL) and Intel oneAPI Data Analytics Library (oneDAL) allows for near-native performance.

Tools for optimising Python code into instructions with more productivity.

Important Python bindings to help your Python project integrate Intel native tools more easily.

Three core packages make up the Data Parallel Extensions for Python:

The NumPy Data Parallel Extensions (dpnp)

Data Parallel Extensions for Numba, aka numba_dpex

Tensor data structure support, device selection, data allocation on devices, and user-defined data parallel extensions for Python are all provided by the dpctl (Data Parallel Control library).

It is best to obtain insights with comprehensive source code level analysis into compute and memory bottlenecks in order to promptly identify and resolve unanticipated performance difficulties in Machine Learning (ML),  Artificial Intelligence ( AI), and other scientific workloads. This may be done with Python-based ML and AI programs as well as C/C++ code using Intel VTune Profiler. The methods for profiling these kinds of Python apps are the main topic of this paper.

Using highly optimised Intel Optimised Numpy and Data Parallel Extension for Python libraries, developers can replace the source lines causing performance loss with the help of Intel VTune Profiler, a sophisticated tool.

Setting up and Installing

1. Install Intel Distribution for Python

2. Create a Python Virtual Environment

   python -m venv pyenv

   pyenv\Scripts\activate

3. Install Python packages

   pip install numpy

   pip install dpnp

   pip install numba

   pip install numba-dpex

   pip install pyitt

Make Use of Reference Configuration

The hardware and software components used for the reference example code we use are:

Software Components:

dpnp 0.14.0+189.gfcddad2474

mkl-fft 1.3.8

mkl-random 1.2.4

mkl-service 2.4.0

mkl-umath 0.1.1

numba 0.59.0

numba-dpex 0.21.4

numpy 1.26.4

pyitt 1.1.0

Operating System:

Linux, Ubuntu 22.04.3 LTS

CPU:

Intel Xeon Platinum 8480+

GPU:

Intel Data Center GPU Max 1550

The Example Application for NumPy

Intel will demonstrate how to use Intel VTune Profiler and its Intel Instrumentation and Tracing Technology (ITT) API to optimise a NumPy application step-by-step. The pairwise distance application, a well-liked approach in fields including biology, high performance computing (HPC), machine learning, and geographic data analytics, will be used in this article.

Summary

The three stages of optimisation that we will discuss in this post are summarised as follows:

Step 1: Examining the Intel Optimised Numpy Pairwise Distance Implementation: Here, we’ll attempt to comprehend the obstacles affecting the NumPy implementation’s performance.

Step 2: Profiling Data Parallel Extension for Pairwise Distance NumPy Implementation: We intend to examine the implementation and see whether there is a performance disparity.

Step 3: Profiling Data Parallel Extension for Pairwise Distance Implementation on Numba GPU: Analysing the numba-dpex implementation’s GPU performance

Boost Your Python NumPy Application

Intel has shown how to quickly discover compute and memory bottlenecks in a Python application using Intel VTune Profiler.

Intel VTune Profiler aids in identifying bottlenecks’ root causes and strategies for enhancing application performance.

It can assist in mapping the main bottleneck jobs to the source code/assembly level and displaying the related CPU/GPU time.

Even more comprehensive, developer-friendly profiling results can be obtained by using the Instrumentation and Tracing API (ITT APIs).

Read more on govindhtech.com

[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.

[pandeypankaj]

Why has Python become so popular? Is Python really that slow?

It has become the de facto language for data science up until today due to a variety of crucial reasons, including readability and simplicity. Python's syntax is clean and readable, hence very well understandable both for beginners and advanced programmers. This readability enhances collaboration and productivity.

Rich Ecosystem

Python comprises an enormous ecosystem of libraries and frameworks focused on data science. It ranges from NumPy and Pandas for efficient numerical computation and manipulation of data, respectively, to Matplotlib and Scikit-learn for visualisation and machine learning, and finally TensorFlow for deep learning.

Community Support

The community of Python users is huge and pretty active, including comprehensive documentation, tutorials, and forums. Online courses are also available. All these resources support learning, problem-solving, and keeping up with changes within the domain. 

It's not only a data science language; it is a general-purpose language

Python finds application in web development, system automation, and many other places. That would mean data scientists can reap the fruits of learning Python in several fields.

Is Python Really That Slow? Software performance bottlenecks, due to Python's interpreted nature, will sometimes arise when working in a compiled language like C++. Yet, consider the following:

Trade-off between Speed and Readability: Usually, Python's emphasis on readability and ease of use overshadows slight performance penalties in many data science applications.

Optimised Libraries: Most of the data science libraries, such as NumPy and Pandas, are hugely optimised by their implementations in C or Fortran, which really helps in giving a performance boost to numeric computational tasks. Parallel Processing-Distributed Computing: Python frameworks like Dask and Ray enable parallel processing and distributed computing, hence enabling a massive performance boost on large-scale data analysis and machine learning tasks.

Hardware Acceleration: Much functionality in Python can be accelerated with hardware; this is particularly the case when it involves deep learning or neural networks.

In summary, Python is among the most popular languages for data science due to its readability, rich ecosystem, community support, and general-purpose nature. These advantages often outweigh the performance concerns, while considering gains in productivity with powerful libraries and tools.

[thesafewaystobereckless]

Hobbies I briefly made my whole ass identity for like 2 days

Essay writing / media commentary

Learning the piano and composition

Fortran video tutorials

Pure python re-implementations of NumPy to prove a bitch wrong

[ubuntutipps]

Installieren Sie die GNU Compiler Collection (GCC) unter Ubuntu 20.04 LTS

Tutorial zur Installation der GNU Compiler Collection (GCC) auf Ubuntu 20.04 LTS Die GNU-Compilersammlung ist eine Liste von Compilern und Bibliotheken für die Programmiersprachen C, C++, Objective-C, Ada, Go, Fortran und D. Mehrere Projekte werden mit GCC kompiliert, daher ist es besser, es auf Ihrem System zu installieren, um zukünftige Fehler zu vermeiden. GCC wurde ursprünglich als Compiler…

[river-gale]

on the other hand it is very charming that if you look up how to calculate planet coordinates you will get tutorials that assume you’re trying to do it in basic or fortran

i am still not over half the "space facts for kids!" websites on the internet being egregiously factually wrong. how dare you make me check my math fifteen times only to realize i'm not the one doing it incorrectly. you're nothing

[musicforprogramming]

AETHER by Mr. Fingers  from the album Cerebral Hemispheres (2018) on Alleviated Music.

Listen to MUSIC FOR PROGRAMMING on SPOTIFY: http://bit.ly/music-4-programming

Released on the Cerebral Hemispheres album, Mr. Finger’s first release since 2005, Aether is a soft, pulsing track that evokes less-taken urban landscape roads.

[oflgtfol]

sometimes im like zomg im so far behind on programming i need to learn twenty thousand more languages still but then i see other people in my year who dont know any programming and its like. ok. nvm

[viswatechynology]

R Software

What is R Software?

R is a programming language and free software developed by Ross Ihaka and Robert Gentleman in 1993. R possesses an extensive catalog of statistical and graphical methods. It includes machine learning algorithms, linear regression, time series, statistical inference to name a few. Most of the R libraries are written in R, but for heavy computational tasks, C, C++ and Fortran codes are preferred.

R is not only entrusted by academic, but many large companies also use R programming language, including Uber, Google, Airbnb, Facebook and so on.

Read More

Data analysis with R is done in a series of steps; programming, transforming, discovering, modeling and communicate the results

Program: R is a clear and accessible programming tool

Transform: R is made up of a collection of libraries designed specifically for data science

Discover: Investigate the data, refine your hypothesis and analyze them

Model: R provides a wide array of tools to capture the right model for your data

Communicate: Integrate codes, graphs, and outputs to a report with R Markdown or build Shiny apps to share with the world

In this introduction tutorial you will learn R

What is R used for?

R by Industry

R package

Communicate with R

Why use R?

Should you choose R?

Is R difficult?

What is R used for?

Statistical inference

Data analysis

Machine learning algorithm

R by Industry :

Read More

If we break down the use of R by industry, we see that academics come first. R is a language to do statistic. R is the first choice in the healthcare industry, followed by government and consulting.

R package

The primary uses of R is and will always be, statistic, visualization, and machine learning. The picture below shows which R package got the most questions in Stack Overflow. In the top 10, most of them are related to the workflow of a data scientist: data preparation and communicate the results.

All the libraries of R, almost 12k, are stored in CRAN. CRAN is a free and open source. You can download and use the numerous libraries to perform Machine Learning or time series analysis.Communicate with R

R has multiple ways to present and share work, either through a markdown document or a shiny app. Everything can be hosted in Rpub, GitHub or the business’s website. Rstudio accepts markdown to write a document. You can export the documents in different formats:

Document :

· HTML

· PDF/Latex

· Word

Presentation :

· HTML

· PDF beamer

Why use R?

Read More

Data science is shaping the way companies run their businesses. Without a doubt, staying away from Artificial Intelligence and Machine will lead the company to fail. The big question is which tool/language should you use?

They are plenty of tools available in the market to perform data analysis. Learning a new language requires some time investment. The picture below depicts the learning curve compared to the business capability a language offers. The negative relationship implies that there is no free lunch. If you want to give the best insight from the data, then you need to spend some time learning the appropriate tool, which is R.

On the top left of the graph, you can see Excel and PowerBI. These two tools are simple to learn but don’t offer outstanding business capability, especially in term of modeling. In the middle, you can see Python and SAS. SAS is a dedicated tool to run a statistical analysis for business, but it is not free. SAS is a click and run software. Python, however, is a language with a monotonous learning curve. Python is a fantastic tool to deploy Machine Learning and AI but lacks communication features. With an identical learning curve, R is a good trade-off between implementation and data analysis.

When it comes to data visualization (DataViz), you’d probably heard about Tableau. Tableau is, without a doubt, a great tool to discover patterns through graphs and charts. Besides, learning Tableau is not time-consuming. One big problem with data visualization is you might end up never finding a pattern or just create plenty of useless charts. Tableau is a good tool for quick visualization of the data or Business Intelligence. When it comes to statistics and decision-making tool, R is more appropriate.

Stack Overflow is a big community for programming languages. If you have a coding issue or need to understand a model, Stack Overflow is here to help. Over the year, the percentage of question-views has increased sharply for R compared to the other languages. This trend is of course highly correlated with the booming age of data science but, it reflects the demand of R language for data science.

In data science, there are two tools competing with each other. R and Python are probably the programming language that defines data science.

Should you choose R?

Data scientist can use two excellent tools: R and Python. You may not have time to learn them both, especially if you get started to learn data science. Learning statistical modeling and algorithm is far more important than to learn a programming language. A programming language is a tool to compute and communicate your discovery. The most important task in data science is the way you deal with the data: import, clean, prep, feature engineering, feature selection. This should be your primary focus. If you are trying to learn R and Python at the same time without a solid background in statistics, its plain stupid. Data scientist are not programmers. Their job is to understand the data, manipulate it and expose the best approach. If you are thinking about which language to learn, let’s see which language is the most appropriate for you.

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The principal audience for data science is business professional. In the business, one big implication is communication. There are many ways to communicate: report, web app, dashboard. You need a tool that does all this together.

Is R difficult?

Years ago, R was a difficult language to master. The language was confusing and not as structured as the other programming tools. To overcome this major issue, Hadley Wickham developed a collection of packages called tidyverse. The rule of the game changed for the best. Data manipulation become trivial and intuitive. Creating a graph was not so difficult anymore.

The best algorithms for machine learning can be implemented with R. Packages like Keras and TensorFlow allow to create high-end machine learning technique. R also has a package to perform Xgboost, one the best algorithm for Kaggle competition.

R can communicate with the other language. It is possible to call Python, Java, C++ in R. The world of big data is also accessible to R. You can connect R with different databases like Spark or Hadoop.

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Finally, R has evolved and allowed parallelizing operation to speed up the computation. In fact, R was criticized for using only one CPU at a time. The parallel package lets you to perform tasks in different cores of the machine.

Summary

In a nutshell, R is a great tool to explore and investigate the data. Elaborate analysis like clustering, correlation, and data reduction are done with R. This is the most crucial part, without a good feature engineering and model, the deployment of the machine learning will not give meaningful results.

[hydralisk98]

Ideas for Servitor

Regular recap of first priorities:

Channel Trailer (Brand name= hydralisk98, Icon/Illustration/Description/Mascot, Who What Why How)

Draw my Life (Recurring theme = Curiosity)

Future Channel Plans (Subliminals, Vlogs, Research Vids)

Overcoming a Personal Struggle (Autism, Attention Deficit Disorder, Procrastination)

Why am I making videos on YouTube (Connect with others, improve production skills, generate and spread gratitude)

Complete tutorial to make videos like me, the entire process (design, script, audio, visuals, video editing, publishing, feedback)

Who What Why How (Channel trailer)

Draw your life (YMCA, JBM, Maisonneuve, Spirituality, Work, Social Welfare, Pandemic, DesRiverains, Internship...)

Future plans (?)

Overcoming struggles (psychic autistic transfem)

Why go online?

Tutorials

Discuss older videos from 2014-~

Channel trailer

Esoteric media production

Graphically designing Klara Ker

Vast personal vision niche meme boards

Collage spree

Affirmation cards

Tarot spreads

Zettelkasten system

SGML + XML/XSL tutorial

HTML5/CSS3 only website challenge

Practicing C programming

Contribute to FLOSS projects on GitHub

Try Landchad.net

Customizing OpenBox into a full theme pack

Common Lisp

F#

Python

Fortran

F*?

Blender

Inkscape

NeoVim

Brave

Nemo

Fish, Zsh and Bash

Midi music compositions

Customizing DCPU-16 & peripherals specs for vintage electronics design

Apocalypse+Wolfenstein Civ 5 CE Meme longplay

Dice making

N64 asset pack design

Making utilities and software toys for PDP-8/E with SimH

Analog video rental store

Make your very own BASIC dialect

Make your very own C dialect

Make your very own Lisp dialect

Reverse-engineering back old standards, hardware and software for production of fair FLOSS alternatives

Fully fair FLOSS full-stack server challenge

Retro computing museum ideas

Animation meme

Neue Geo Syndicalism and how to combat Wilsonism & Nazism timelessly

Helluva Boss

Jucika

Ion Fury

Making customized keypads

Learning mainstream languages (German, Spanish, Russian...)

Learning sidestream languages (Hungarian, Galatian and Chaldean)

ZealOS

World design workflows using randomizers, roll tables and automaton

Playing Cataclysm Dark Days Ahead, Dwarf Fortress and NetHack

FreeCiv several AI-only timelapses (~1440 turns each)

Optimization maniac

Catalog making

Paper calendars and time trackers

Rolodex

QGIS modelling

Opinions of using niche open source social media and tools vs proprietary ones

Conlang making

WW1 ramblings about Russia (Choosing between radical red and nationalists is hard)

Ken Silverman's Build2

Plan9/ & Uxn

Dis virtual machine shenanigans

Making a few Panini / Turing / Lambda -complete toys

360 degrees rollercoaster

Quake 1 played with comrades

Curations of the month as media compilations

Deep dive into CTSS and ITS

Portrait paper models

Journals of my mindflows

Live-streamed explorations of techs

[govindhtech]

New AMD ROCm 6.3 Release Expands AI and HPC Horizons

Opening Up New Paths in AI and HPC with AMD’s Release ROCm 6.3. With the introduction of cutting-edge tools and optimizations to improve AI, ML, and HPC workloads on AMD Instinct GPU accelerators, ROCm 6.3 represents a major milestone for the AMD open-source platform. By increasing developer productivity, ROCm 6.3 is designed to enable a diverse spectrum of clients, from cutting-edge AI startups to HPC-driven businesses.

This blog explores the release’s key features, which include a redesigned FlashAttention-2 for better AI training and inference, the introduction of multi-node Fast Fourier Transform (FFT) to transform HPC workflows, a smooth integration of SGLang for faster AI inferencing, and more. Discover these fascinating developments and more as ROCm 6.3 propels industry innovation.

Super-Fast Inferencing of Generative AI (GenAI) Models with SGLang in ROCm 6.3

Industries are being revolutionized by GenAI, yet implementing huge models frequently involves overcoming latency, throughput, and resource usage issues. Presenting SGLang, a new runtime optimized for inferring state-of-the-art generative models like LLMs and VLMs on AMD Instinct GPUs and supported by ROCm 6.3.

Why It Is Important to You

6X Higher Throughput: According to research, you can outperform current systems on LLM inferencing by up to 6X, allowing your company to support AI applications on a large scale.

Usability: With Python integrated and pre-configured in the ROCm Docker containers, developers can quickly construct scalable cloud backends, multimodal processes, and interactive AI helpers with less setup time.

SGLang provides the performance and usability required to satisfy corporate objectives, whether you’re developing AI products that interact with customers or expanding AI workloads in the cloud.

Next-Level Transformer Optimization: Re-Engineered FlashAttention-2 on AMD Instinct

The foundation of contemporary AI is transformer models, although scalability has always been constrained by their large memory and processing requirements. AMD resolves these issues with FlashAttention-2 designed for ROCm 6.3, allowing for quicker, more effective training and inference.

Why It Will Be Favorite by Developers

3X Speedups: In comparison to FlashAttention-1, achieve up to 3X speedups on backward passes and a highly efficient forward pass. This will speed up model training and inference, lowering the time-to-market for corporate AI applications.

Extended Sequence Lengths: AMD Instinct GPUs handle longer sequences with ease with to their effective memory use and low I/O overhead.

With ROCm’s PyTorch container and Composable Kernel (CK) as the backend, you can easily add FlashAttention-2 on AMD Instinct GPU accelerators into your current workflows and optimize your AI pipelines.

AMD Fortran Compiler: Bridging Legacy Code to GPU Acceleration

With the release of the new AMD Fortran compiler in ROCm 6.3, businesses using AMD Instinct accelerators to run historical Fortran-based HPC applications may now fully utilize the potential of contemporary GPU acceleration.

Principal Advantages

Direct GPU Offloading: Use OpenMP offloading to take advantage of AMD Instinct GPUs and speed up important scientific applications.

Backward Compatibility: Utilize AMD’s next-generation GPU capabilities while building upon pre-existing Fortran code.

Streamlined Integrations: Connect to ROCm Libraries and HIP Kernels with ease, removing the need for intricate code rewrites.

Businesses in sectors like weather modeling, pharmaceuticals, and aerospace may now leverage the potential of GPU acceleration without requiring the kind of substantial code overhauls that were previously necessary to future-proof their older HPC systems. This comprehensive tutorial will help you get started with the AMD Fortran Compiler on AMD Instinct GPUs.

New Multi-Node FFT in rocFFT: Game changer for HPC Workflows

Distributed computing systems that scale well are necessary for industries that depend on HPC workloads, such as oil and gas and climate modeling. High-performance distributed FFT calculations are made possible by ROCm 6.3, which adds multi-node FFT functionality to rocFFT.

The Significance of It for HPC

The integration of the built-in Message Passing Interface (MPI) streamlines multi-node scalability, lowering developer complexity and hastening the deployment of distributed applications.

Scalability of Leadership: Optimize performance for crucial activities like climate modeling and seismic imaging by scaling fluidly over large datasets.

Larger datasets may now be processed more efficiently by organizations in sectors like scientific research and oil and gas, resulting in quicker and more accurate decision-making.

Enhanced Computer Vision Libraries: AV1, rocJPEG, and Beyond 

AI developers need effective preprocessing and augmentation tools when dealing with contemporary media and datasets. With improvements to its computer vision libraries, rocDecode, rocJPEG, and rocAL, ROCm 6.3 enables businesses to take on a variety of tasks, from dataset augmentation to video analytics.

Why It Is Important to You

Support for the AV1 Codec: rocDecode and rocPyDecode provide affordable, royalty-free decoding for contemporary media processing.

GPU-Accelerated JPEG Decoding: Use the rocJPEG library’s built-in fallback methods to perform image preparation at scale with ease.

Better Audio Augmentation: Using the rocAL package, preprocessing has been enhanced for reliable model training in noisy situations.

From entertainment and media to self-governing systems, these characteristics allow engineers to produce more complex AI solutions for practical uses.

It’s important to note that, in addition to these noteworthy improvements, Omnitrace and Omniperf which were first released in ROCm 6.2 have been renamed as ROCm System Profiler and ROCm Compute Profiler. Improved usability, reliability, and smooth integration into the existing ROCm profiling environment are all benefits of this rebranding.

Why ROCm 6.3?

AMD With each release, ROCm has advanced, and version 6.3 is no different. It offers state-of-the-art tools to streamline development and improve speed and scalability for workloads including AI and HPC. ROCm enables companies to innovate more quickly, grow more intelligently, and maintain an advantage in cutthroat markets by adopting the open-source philosophy and constantly changing to satisfy developer demands.

Are You Prepared to Jump? Examine ROCm 6.3‘s full potential and discover how AMD Instinct accelerators may support the next significant innovation in your company.

Read more on Govindhtech.com

[techtutor]

Sum,Subtraction,Multiplication and Division in fortran program

[polhsub]

Macports gfortran

#MACPORTS GFORTRAN HOW TO#

#MACPORTS GFORTRAN FOR MAC OS X#

#MACPORTS GFORTRAN MAC OS X#

#MACPORTS GFORTRAN INSTALL#

* conflicts with dragonegg33 dragonegg34 g95 gcc44 gcc46 gcc47 gcc48 gcc49 gcc5 gcc6 Gcc45: Build using the MacPorts gcc 4.5 compiler * conflicts with dragonegg33 dragonegg34 g95 gcc45 gcc46 gcc47 gcc48 gcc49 gcc5 gcc6 Gcc44: Build using the MacPorts gcc 4.4 compiler * conflicts with dragonegg33 dragonegg34 gcc44 gcc45 gcc46 gcc47 gcc48 gcc49 gcc5 gcc6 G95: Build using the g95 Fortran compiler * conflicts with dragonegg33 g95 gcc44 gcc45 gcc46 gcc47 gcc48 gcc49 gcc5 gcc6 * conflicts with dragonegg34 g95 gcc44 gcc45 gcc46 gcc47 gcc48 gcc49 gcc5 gcc6ĭragonegg34: Build using the MacPorts dragonegg 3.4 compiler For instance, to build GCC 4.9, use the commandĭragonegg33: Build using the MacPorts dragonegg 3.3 compiler To provide a particular command with administrator privileges (the ultimate administrator of a UNIX operating system is called the root user), use the sudo command.

#MACPORTS GFORTRAN INSTALL#

To install ports, you need root access (administrator privileges). Programs are stored in /opt/local/bin, libraries are stored in /opt/local/lib, and include header files are stored in /opt/local/include. Ports are installed in the prefix /opt/local. The package name itself is gcc43 and that is the name that MacPorts will recognize that port with. For example, the first port matching the characters "gcc4" is gcc43, version number 4.3.6_11. In the above list, port versions are listed next to their names following the sign. This port is only a stub and has been made obsolete by gcc5 For example, suppose we want to find which versions of the gcc compiler are available. You don't know the exact name that MacPorts uses for that program.You want to know if MacPorts has a port for your program.You'll often find yourself wanting to search for specific packages for a number of reasons. You can download XQuartz here Looking for packages Instead, you have to download the XQuartz program.

#MACPORTS GFORTRAN MAC OS X#

Starting with Mac OS X 10.8, Apple stopped including X11 with their OS. Running the following command in the terminal installs the command-line tools: After this, you need to install the command-line tools. Read through the license (or type q to skip through it), and type agree when prompted. So run the following command in a terminal You can download this from the Mac App store (just search for "xcode").Īfter installing XCode, you need to agree to the license. Installing MacPortsīefore installing MacPorts, you will have to install Apple's developer tools (Xcode). The tutorial below deals with the command-line interface to MacPorts. PortAuthority), but since you need to use the command line for most computational chemistry software, you should probably just stick with the command-line for MacPorts as well. MacPorts is used completely using the command line (use the Terminal program that ships with Mac OS X or some alternative terminal program). MacPorts is a package manager (like yum in Red Hat/CentOS/Fedora or aptitude in Debian/Ubuntu) that manages software by compiling packages and their dependencies from source code.

#MACPORTS GFORTRAN FOR MAC OS X#

If you just wish to set your Mac up to run Amber, look at the "Installing MacPorts" section below (especially for Mac OS X Lion), and the "Preparing for AMBER" section near the bottom. The second describes installing the necessary packages to build and run Amber. One of the main reasons I like MacPorts better, however, is their selection abilities (you can install different versions of compilers, for instance, and let MacPorts manage symlinks to them), and they build to /opt/local rather than /usr/local (reducing the chance that you will see homebrew files getting in the way). There are alternatives, with homebrew being the most popular.

#MACPORTS GFORTRAN HOW TO#

The first section describes how to use and set up my package manager of choice, MacPorts (previously DarwinPorts). OS X is, after all, built on a registered UNIX kernel-Darwin. It is more an overview of using a particular package manager to build and maintain Open Source software from the UNIX world on your Mac computer. However, this setup is applicable to many other computational chemistry/physics packages as well. This page describes how to set up Mac OS X to run Amber specifically.

[trickslong]

Approximatrix simply fortran with pgplot

#APPROXIMATRIX SIMPLY FORTRAN WITH PGPLOT SOFTWARE#

#APPROXIMATRIX SIMPLY FORTRAN WITH PGPLOT CODE#

#APPROXIMATRIX SIMPLY FORTRAN WITH PGPLOT WINDOWS#

#APPROXIMATRIX SIMPLY FORTRAN WITH PGPLOT SOFTWARE#

You will get access to a powerful editor using Simply Fortran. This way you will be able to manage your projects professionally using the software. This means that this software, due to its professional capabilities, is able to manage and edit your projects better than ever.Īmong the features of this product, we can mention the integration in the performance of this software. The integration in this software makes you simplify your large, heavy and advanced projects due to the use of the simple development environment of this software. Also, the software in front of you has fully complied with the existing standard in your field of work. In addition to standards, the software supports OpenMP and allows the development of Fortran parallel code. Setting breakpoints, examining variables, and navigating the call stack are all easy tasks.Simply Fortran is the name of the comprehensive and powerful Approximatrix Group editing software for the Fortran programming language. This software is referred to as a complete and, of course, reliable Fortran compiler with the necessary productivity tools that specialists need. The package in front of you includes a configured Fortran compiler, an integrated development environment, including an integrated debugging, and a host of other development needs. Simply Fortran provides source-level debugging facilities directly in the integrated development environment. Additionally, all project issues can be quickly examined and updated via the Project Issues panel.

#APPROXIMATRIX SIMPLY FORTRAN WITH PGPLOT CODE#

Simply Fortran highlights compiler warnings and errors within the editor as the source code is updated. For new users, a step-by-step tutorial is also available. A crash that could be caused by searching the current tab from the toolbars search box has been. The latest release fixes a number of minor issues with the development environment and incorporates some incremental enhancements requested by users. Users can quickly access documentation from the Help menu in Simply Fortran. Version 3.24 of Simply Fortran is now available from Approximatrix. You might be able to use other plotting software, PLPlot or PGPlot, with their Fortran interfaces. However, AppGraphics is quite low-level, and you'd have some work ahead of you. Included with Simply Fortran is documentation for both the integrated development environment and the Fortran compiler. With Simply Fortran for Windows, you could manually generate the color map by writing routines for AppGraphics. Simply Fortran provides autocompletion for Fortran derived types, available modules, and individual module components. Quickly create and display two-dimensional bar, line, or scatter charts from Fortran routines with ease.

#APPROXIMATRIX SIMPLY FORTRAN WITH PGPLOT WINDOWS#

Both targets are available on all Windows platforms. Designed from the beginning for the Fortran language, Simply Fortran delivers a reliable Fortran compiler on Windows platforms with all the necessary productivity tools that professionals expect. Simply Fortran is a complete Fortran solution for Microsoft Windows and compatible operating systems. Additionally, Simply Fortran can be used on platforms compatible with Microsoft Windows, including WINE. Simply Fortran for Windows can produce both 32-bit and 64-bit code with its included compiler. A Professional Fortran Development Environment. Both 32-bit and 64-bit desktops are supported. Simply Fortran runs perfectly on versions of Microsoft Windows from Windows XP through Windows 8. When an intrinsic function or subroutine is encountered, the documentation for that procedure will be displayed as well. While entering Fortran code, Simply Fortran provides call tips for functions and subroutines declared within a user's project. The Simply Fortran package includes a configured GNU Fortran compiler installation, an integrated development environment, a graphical debugger, and a collection of other development necessities.Īpproximatrix Simply Fortran is an inexpensive way for anyone to productively develop using the Fortran language. Simply Fortran delivers a reliable Fortran compiler on Windows platforms with all the necessary productivity tools that professionals expect. Simply Fortran is a new, complete Fortran solution designed from the beginning for interoperability with GNU Fortran.