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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Multithreading vs. Multiprocessing: Understanding the Differences

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Analytics Vidhya Last Updated : 06 Nov, 2023
4 min read

Multithreading and multiprocessing are fundamental concepts in computer multitasking, enabling concurrent execution of tasks. While both aim to improve system performance, they have distinct characteristics and are suitable for different scenarios. This article will explore multithreading vs. multiprocessing, their advantages, disadvantages, and the factors influencing their use in various programming tasks. Let’s delve into the intricacies of these powerful techniques to gain a comprehensive understanding of their applications and implications in modern computing.

Table of contents

Multithreading vs. Multiprocessing: Understanding the Differences
Source: Educba

What is Multiprocessing?

The multiprocessing approach has its own memory area, stack, and program counter and runs independently. Pipelines, shared memory, and message transmission are just a few ways processes can talk to one another. A multiprocessing module in Python allows controlling several processes easily. Hence, the concept of executing several processes simultaneously on a computer system in addition to several processing units refers to multiprocessing.

What is Multithreading?

The process of Multithreading includes running several threads concurrently. A thread represents an independent path of execution within a program, sharing the same memory space and resources. Using shared data structures or synchronization techniques, threads in a process can talk to one another. You may build and manage threads using the threading module in Python.

Multithreading vs. Multiprocessing

We shall compare Multiprocessing and Multithreading approaches in the following table to grasp their differences better:

CharacteristicsMultithreadingMultiprocessing
ExecutionConcurrentParallel
Resource UtilizationShared resourcesSeparate resources
MemoryShared memory spaceIndependent memory
CommunicationLightweightRequires inter-process communication mechanisms
CPU-bound TasksLess efficientMore efficient
I/O-bound TasksEfficientEfficient
ComplexityLower complexityHigher complexity
DebuggingEasierMore challenging

Also Read: CPU vs GPU – What is the difference?

Features of Multithreading vs. Multiprocessing

Characteristics of Multiprocessing

  • Parallel Execution: Multiprocessing allows simultaneous execution of processes on separate cores or processors, improving system throughput and performance.
  • Independent Processes: In multiprocessing, each process has its memory, stack, and program counter. This ensures that when one process crashes or fails, it doesn’t influence the other processes.
  • Resource Allocation: Multiprocessing assigns distinct resources to each process, allowing for effective resource utilization and preventing conflicts.
  • Inter-Process Communication: Processes in multiprocessing can communicate through mechanisms like pipelines, shared memory, or message passing, facilitating data exchange and synchronization.
  • CPU-bound Efficiency: By instilling multiple cores or processors, multiprocessing is effective for CPU-bound workloads, resulting in much faster execution of computationally extensive operations.
  • Complexity: Since inter-process communication, synchronization, and coordination are to be managed, multiprocessing is much more complex than single-threaded execution.

Characteristics of Multithreading

  • Concurrent Execution: Multiple threads can run simultaneously within a single process thanks to Multithreading.
  • Shared Memory Space: A process’s threads can access shared data directly since they share the same memory space.
  • Resource Sharing: Threads can share resources, including memory, file handles, and network connections, thanks to Multithreading.
  • Lightweight Communication: Communication between threads is lightweight and efficient since they can directly access shared data.
  • I/O-bound Efficiency: Multithreading is effective for I/O-bound tasks as threads can overlap waiting times and improve overall performance.
  • Lower Complexity: Multithreading has lower complexity than Multiprocessing, as shared memory simplifies communication between threads.
  • Synchronization Challenges: Synchronization between threads can introduce complexities like race conditions and deadlocks, requiring careful management and synchronization mechanisms.

Benefits of Multithreading vs. Multiprocessing

Advantages of Multiprocessing

  1. Enhanced Fault Tolerance: Each Multiprocessing process runs independently, improving fault tolerance. If one process encounters an error or crashes, others can continue their execution unaffected.
  2. Resource Isolation: Multiprocessing provides resource isolation between processes. Each process has its memory space, stack, and program counter, preventing interference and conflicts between processes.

Advantages of Multithreading

  1. Improved Performance for I/O-bound Tasks: Multithreading benefits I/O-bound tasks where threads overlap waiting times. By executing multiple threads concurrently, the overall performance can be enhanced by utilizing idle CPU time efficiently.
  2. Lightweight Communication: Communication between threads in Multithreading is lightweight, as they can directly access shared memory. This makes data sharing between threads quick and efficient without complicated communication techniques.

Multithreading vs. Multiprocessing: Disadvantages

Disadvantages of Multiprocessing

  1. Complexity of Inter-Process Communication: Multiprocessing adds complexity since inter-process communication methods are required. Pipes, shared memory, or message passing are communication channels that can be implemented and managed to reduce overhead and increase system complexity.
  2. Increased Memory Overhead: When opposed to Multithreading, Multiprocessing can have higher memory overhead because each process needs its dedicated memory area. This is especially important when managing numerous processes.

Disadvantages of Multithreading

  1. Synchronization Challenges: Multithreading introduces challenges such as race conditions and deadlocks when multiple threads concurrently access and modify shared data. Appropriate synchronization methods such as locks and semaphores must be utilized to avoid data corruption and guarantee consistency.
  2. Debugging Complexity: Programming for multiple threads can be more challenging than multiprocessing. Multithreaded programming can be challenging to discover and fix mistakes in because of non-deterministic behavior, race scenarios, and thread interactions, necessitating rigorous debugging techniques and tools.

Conclusion

Mastering the intricacies of multithreading and multiprocessing is crucial to harnessing the full potential of data processing and analysis. As we explored the differences and applications of these parallel computing techniques, one thing becomes clear: data scientists must equip themselves with the right skills to stay ahead in this dynamic field.

Analytics Vidhya’s Blackbelt program offers a comprehensive and immersive learning experience, empowering aspiring data scientists to dive deep into multithreading and multiprocessing. With expert guidance and hands-on practice, participants will learn to leverage these powerful techniques to boost data processing efficiency, tackle large-scale computations, and accelerate their data science projects.

Chekcout: Why GPUs are more suited for Deep Learning?

Frequently Asked Questions

Q1: What factors determine whether to use multithreading or multiprocessing in a task?

A. The choice between multithreading and multiprocessing depends on task requirements. Multithreading suits input/output operations and waiting times, enhancing performance. Multiprocessing is ideal for computationally complex tasks, dividing them into parts for concurrent processing.

Q2: How do multiprocessing, multithreading, and multicore differ in their operational characteristics?

A. Multiprocessing handles independent processes with separate memory, while multithreading runs threads sharing memory within a process. Multicore refers to hardware with multiple processor cores for simultaneous execution, improving overall performance.

Q3: What is the distinction between multitasking and multithreading?

A. Multitasking manages multiple processes in an operating system, allowing them to run concurrently. Multithreading enables parallel execution of threads within a single process, enhancing resource utilization and efficiency.

Q4: In Python, how does multithreading handle threads within a process?

A. Python’s multithreading manages threads within a process, utilizing shared memory for communication. Multiprocessing sets up and controls multiple independent processes with their own memory. Python offers “threading” and “multiprocessing” modules for interacting with threads and processes.

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Analytics Vidhya

Analytics Vidhya Content team

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Top 100 Data Science Interview Questions & Answers 2024
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