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Author: Afzal Badshah, PhD

MPI Gather Function in Python

MPI Gather Function in Python

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The gather function is used to gather data from multiple processes into a single process. We’ll go through the provided code, line by line, and understand how the gather function works. The detailed tutorial of MPI with a python can be visited here. Code Explanation This line imports the MPI functionality from the mpi4py library. These lines initialize the MPI communicator (comm) and obtain the total number of processes (size) and the rank of the current process (rank). Each process…

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MPI with Python: Calculating Squares of Array Elements Using Multiple Processors

MPI with Python: Calculating Squares of Array Elements Using Multiple Processors

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In this lab tutorial, we will explore how to utilize multiple processors to compute the squares of elements in an array concurrently using the MPI (Message Passing Interface) library in Python, specifically using the mpi4py module. MPI is a widely-used standard for parallel computing in distributed memory systems. We’ll create a master-worker model where the master process distributes tasks to worker processes, each responsible for computing the square of a subset of the array elements. The detailed tutorial of MPI…

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Data Manipulation with MongoDB Aggregation Framework in Python

Data Manipulation with MongoDB Aggregation Framework in Python

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MongoDB Aggregation Framework is a powerful tool that allows for data manipulation and analysis within MongoDB collections. It provides a flexible and efficient way to process and transform data, enabling users to perform complex operations such as grouping, sorting, filtering, and computing aggregate values. In this lab tutorial, we will introduce the concepts of MongoDB Aggregation Framework, provide a detailed explanation of the code, and walk through each line to understand its functionality. Visit the detailed tutorial here. Code Connection…

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Big Data Technologies

Big Data Technologies

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Big Data refers to datasets that are too large and complex for traditional data processing applications to handle efficiently. It is characterized by the 5 Vs: Volume, Velocity, Variety, Veracity, and Value. Volume refers to the vast amount of data generated, Velocity refers to the speed at which data is generated and processed, Variety refers to the different types of data (structured, semi-structured, and unstructured), Veracity refers to the reliability and quality of the data, and Value refers to the…

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Data Modeling and Feature Engineering

Data Modeling and Feature Engineering

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Data modelling is the cornerstone of successful data analysis and machine learning projects. It’s the crucial first step where you define the structure and organization of your data. Just imagine a construction project – before you start building, you need a blueprint to ensure everything fits together. Data modeling acts as the blueprint for your data, organizing it in a way that facilitates efficient exploration and model building. Here you can visit the detailed tutorial. This process involves selecting a…

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Introduction to Parallel Programming Languages: Unlocking the Power of Multiple Processors

Introduction to Parallel Programming Languages: Unlocking the Power of Multiple Processors

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As data sizes and computational demands grow, traditional sequential programming approaches often reach their limits. Parallel programming languages offer a solution by enabling us to harness the power of multiple processors simultaneously, significantly accelerating computations. This tutorial looks into the fundamentals of parallel programming languages, equipping you for the exciting world of parallel and distributed computing. You can visit the detailed tutorial here. Sequential vs. Parallel Programming: Understanding the Divide Sequential Programming: The traditional approach where instructions are executed one…

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Blocking and Non-blocking Communication in MPI

Blocking and Non-blocking Communication in MPI

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In parallel computing with MPI (Message Passing Interface), communication between processes plays a crucial role in achieving efficient parallelization of algorithms. Two common approaches to communication are blocking and non-blocking communication. You can visit the detailed tutorial on MPI with Python here. Blocking Communication Blocking communication involves processes halting their execution until the communication operation is complete. In MPI, blocking communication functions like comm.send() and comm.recv() ensure that the sender waits until the receiver receives the message, and vice versa….

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Visualizing Data from MongoDB Collection using Python

Visualizing Data from MongoDB Collection using Python

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Data visualization is a crucial aspect of data analysis, allowing us to gain insights and make informed decisions. MongoDB, a popular NoSQL database, offers flexibility in storing and retrieving data, making it a preferred choice for many applications. In this tutorial, we will explore how to visualize data retrieved from a MongoDB collection using Python. You can visit the detailed tutorial on MongoDB and Data Science here. Code Overview Below is the Python code to connect to a MongoDB Atlas…

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Shared and Distributed Memory in Parallel Computing

Shared and Distributed Memory in Parallel Computing

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In parallel and distributed computing, memory management becomes crucial when dealing with multiple processors working together. Two prominent approaches exist: shared memory and distributed memory. This tutorial will delve into these concepts, highlighting their key differences, advantages, disadvantages, and applications. Visit the detailed tutorial on Parallel and Distributed Computing. Shared Memory Shared memory systems provide a single, unified memory space accessible by all processors in a computer. Imagine a whiteboard where multiple people can write and read simultaneously. Physically, the…

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Introduction to Machine Learning

Introduction to Machine Learning

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Machine Learning (ML) is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and models that allow computers to learn from data and make predictions or decisions without being explicitly programmed for every task. The primary goal of machine learning is to develop algorithms that can learn patterns and relationships from data and use this knowledge to make predictions or decisions on new, unseen data. Imagine a dataset containing information about students’ attendance records, study hours…

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