Do not follow Old Python Stories!.
Lakshminarasimhan S.
StoryListener | Polymath | PoliticalCritique | AgenticRAG Architect | Strategic Leadership | R&D
"Python is an interpreter and it is slow".?Do not follow these kind of old story.?
It’s not Your Fault
It’s understandable why Python developers have the wrong idea about concurrency.
Any google search will tell you why.
You have to deal with:
There are gems out there, but they are hard to find, buried and spread all over the web.
Something has to change.
If you want to do high-performance computing, Python is a viable option today.
To solve any kind of data-mining problem, Python is an intelligent way.?
Python is easy to learn. it is very much holistic.?Python Comprehensions and Expressions are very cool.
Interoperability inside python is superb - such as c, c++, cython, jython, xlwings (vba), rpy (R), sl4a (android scripting), pyhs2(hive), pyspark(spark), matplotlib(matlab), streamlit, panel, d3js, mayavi2(maya 3D), and many more..?
The Anaconda team has created an “R Essentials” bundle with the IRKernel and over 80 of the most used R packages for data science.?
Python is very good in support on cross platform on Linux, Windows, Mac, iOS, Android.
NLK is a platform on python to work with human language data.?NLTK has built-in support for dozens of corpora and trained models.
Computer vision is platform to work with images and video content, solve object detection, face recognition, activity recognition, posture estimation, gait analysis and more. Scikit-Images helps to process opto-thermal images. Mahotas, pycairo,pillow and many more tools are in the image processing stack.
Tkinter, GTK+, pyQT, KiWi and many GUI Frameworks are available for GUI developers.
Scientists and Software engineers could ingest data in any transfer protocol. Python is capable of reading any matter.?
Also build web applications on a Python framework called Django. Examples: Facebook, Instagram, Firefox, Pinterest, even YouTube!
Completely free enterprise-ready Python distributions available?for large-scale data processing (Bigdata), predictive analytics, and scientific computing.
Python is taught at lot of premier?institutions?e.g., MIT
Huge crowd is using Python, i.e acceptance and development among third party developers across world.
领英推荐
There are 330 Managed Python?Packages?available open source?
Day-to-Day Small things in Python needs very less effort where as what takes at least thrice as long in other languages.
PythonOS is widely used in Embedded Application,
Python has vast opportunity on Internet of Anything(IoAT), Python barely runs on metal, Micropython + pyBoard/Rashberry Pi/Adruino?
You can control the bulb in your living room using a web browser with the help of python.
Using wipy, Viper?you can completely design and automate your own IoT. It comes with viper IoT suite.
Tensorflow, Keras, Pytorch and Gensim are added as gems to solve any language + vision problem.
People say python is slow and not robust. They did not change and updated themselves to modern technology. Numba, CuPy, Scikit-CUDA, RAPIDS, PyCUDA, PyTorch, or TensorFlow, pySpark and more existing new developments, drastically transformed python and it’s top of top of top, 1st of all languages for accelerated computing.
Read more here about the PTX (Parallel thread execution) in python. A nice SAXPY is here.
This post is a GPU program chrestomathy. What’s a Chrestomathy, you ask?
In?computer?programming, a?program chrestomathy?is a collection of similar programs written in various?programming languages, for the purpose of demonstrating differences in syntax, semantics and idioms for each language.?[Wikipedia]
There are several good examples of program chrestomathies on the web, including?Rosetta Code?and?NBabel, which demonstrates?gravitational N-body simulation?in multiple programming languages. In this post I demonstrate six ways to implement a simple SAXPY computation on the CUDA platform.?Why is this interesting? Because it demonstrates the breadth of options you have today for programming NVIDIA GPUs, and it covers the three main approaches to GPU computing: GPU-accelerated libraries, GPU compiler directives, and GPU programming languages.
Python really rocks!..
Best technology is python for building backend systems?that we really love python.
Last edited date: 16th November 2021
Dear Lakshminarasimhan, Thanks for this article... I learnt the power of Python ~10 years back, when one of the very learned colleague of mine at GE and a Purdue PhD wrote a script in Python for Computer aided simulations for extracting data for calculations as part of his GB project which I was bless to mentor... Those days large data analysis was not even talked.. His code was so powerful it did do the calculations in very shortest time by accounting millions of data, orientation etc... I am proud he was an alma matter from IIT, very great guy whom I respect for his knowledge... When you mentioned usage of Python for Scientist in your post, my friend came into my mind with his code in python for very large applications