Zachary Palmer

Zachary Palmer


Parallel computing in ReactJS

User is working with your application, suddenly, UI freezes and probably, one of the CPU cores is burning! They cannot do anything. The only perception you can feel is as hot as a hell metal case of the laptop. Although this sounds like a horror movie, this is your application that cannot leverage modern APIs to lift heavy computation to a different thread where consequently user suffers the pain.

Modern features like Web Workers, WebAssembly, Worklets, and Service Worker allow us to leverage multithreading computing to run tasks parallely which at the end, makes the user feel like in a rainbow paradise instead of a nightmare, even though JavaScript is a single-threaded programming language!

In this session, I am going to show my experience running jobs in parallel on a React.Js application that will provide a pleasant user experience and exciting development.

#reactjs #react-js #javascript #web-development

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Parallel computing in ReactJS
Paula  Hall

Paula Hall


Making Pandas fast with Dask parallel computing

So you, my dear Python enthusiast, have been learning Pandas and Matplotlib for a while and have written a super cool code to analyze your data and visualize it. You are ready to run your script that reads a huge file and all of a sudden your laptop starts making un ugly noise and burning like hell. Sounds familiar?

Well, I have got a couple of good news for you: this issue doesn’t need to happen anymore and you no, you don’t need to upgrade your laptop or your server.

Introducing Dask:

Dask is a flexible library for parallel computing with Python. It provides multi-core and distributed parallel execution on larger-than-memory datasets. It figures out how to break up large computations and route parts of them efficiently onto distributed hardware.

A massive cluster is not always the right choice

Today’s laptops and workstations are surprisingly powerful and, if used correctly, can handle datasets and computations for which we previously depended on clusters. A modern laptop has a multi-core CPU, 32GB of RAM, and flash-based hard drives that can stream through data several times faster than HDDs or SSDs of even a year or two ago.

As a result, Dask can empower analysts to manipulate 100GB+ datasets on their laptop or 1TB+ datasets on a workstation without bothering with the cluster at all.

The project has been a massive plus for the Python machine learning Ecosystem because it democratizes big data analysis. Not only can you save money on bigger servers, but also it copies the Pandas API so you can run your Panda script changing very few lines of code.

#making pandas fast with dask parallel computing #dask parallel computing #pandas #pandas fast #dask #dask parallel

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Parallel Programming: Multiprocessing in Python

In this era of computation power greed, we tend to forget to use the power we can utilize on our very computers

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The hunger for computation power among programmers, gamers, scientists, software developers, and most humans that know how to use a computer, in general, is immense. We are always looking for applications that are less compute-intensive and more efficient. This allows us to make use of our computer setups more efficiently.

However, many of us do not fully utilize the computation power already available to us on our computers. Utilizing this power when needed can lead to exponentially better performances and usually, you can run the processes 2–3 times faster with some changes in code. How do we do this you ask? Well, let’s dive in.

This blog focuses on parallel programming. i.e, running a program on multiple processors simultaneously. When you run your program it usually uses one of the cores in your computer. However, most computers have multiple cores. Depending on your processor it maybe dual-core, quad-core, octa-core, or may contain more cores. If (let’s say) you have a quad-core processor running a program only on one core, you are essentially letting go of the other three cores and hence three times the computation power you are using. Using all these cores can theoretically speed up your tasks by four.

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However, it is not so simple otherwise software companies would all be using all the cores all the time for better performance. If you want to increase the performance of your program you need to make sure that it can be parallelized. i.e, it can be run on different cores at once simultaneously.

Let us take a simple example to understand this point. Let us say you have to build a product and you divide its manufacturing into four stages. If you can go from stage 1 to stage 2 only when stage 1 is already completed, and then from stage 2 to stage 3 only when stage 2 is completed and so on. Then this process is a sequential process since you have to follow a sequence to execute your instructions. However, If you can break the manufacturing into four parts and assign it to different workers, then the process can be parallelized. e.g, building four components for a toy that can be joined once they are finished.

Once you have established that your program can be parallelized, the next step is to write the code for it. I will not write the code in this blog, however, the code for this blog can be found at this Github repo. I choose python to write the code and I used the multiprocessing module to run the program on multiple processors.

In this program, we will see two applications of parallel programming. First is Matrix Multiplication which can be easily parallelized and next we shall see prefix sum scan, which on the first look seems to be a sequential problem but can be parallelized to run on multiple processors.

#programming #computer-science #computers #gpu #parallel-computing

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Why ReactJS is better for Web Application Development?

Web Application Development is essential for a business in today’s digital era. Finding the right platform for Web Application Development is important for building an effective Web Application that can enhance the overall customer engagement. Here’s what makes ReactJS a better option for building your next Web Application.

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