Parallel Processing Concepts – in Computer Architecture

What You’ll Learn 📚

• Core concepts of parallel processing
• Key terminology explained simply
• Practical examples to illustrate concepts
• Common questions and their answers
• Troubleshooting common issues

Introduction to Parallel Processing

Parallel processing is like having multiple chefs in a kitchen, each preparing a different part of a meal at the same time. This allows the meal to be ready faster than if one chef did everything alone. In computer terms, it’s about using multiple processors or cores to perform tasks simultaneously, improving efficiency and speed.

Core Concepts

• Concurrency: The ability to run multiple tasks or processes at the same time.
• Parallelism: A type of concurrency where tasks are literally running at the same time on multiple processors.
• Multithreading: A technique where a single set of code can be used by several processors at different stages of execution.

Key Terminology

• Thread: The smallest unit of processing that can be scheduled by an operating system.
• Core: A single processing unit within a CPU. Modern CPUs have multiple cores.
• Process: A program in execution, which can contain multiple threads.

Simple Example: Cooking Dinner 🍽️

Progressively Complex Examples

Example 1: Multithreading in Python

import threading
def print_numbers():
    for i in range(5):
        print(i)

def print_letters():
    for letter in 'abcde':
        print(letter)

# Creating threads
thread1 = threading.Thread(target=print_numbers)
thread2 = threading.Thread(target=print_letters)

# Starting threads
thread1.start()
thread2.start()

# Joining threads to wait for them to finish
thread1.join()
thread2.join()

Example 2: Parallel Processing in Java

public class ParallelExample {
    public static void main(String[] args) {
        Runnable printNumbers = () -> {
            for (int i = 0; i < 5; i++) {
                System.out.println(i);
            }
        };

        Runnable printLetters = () -> {
            for (char letter = 'a'; letter <= 'e'; letter++) {
                System.out.println(letter);
            }
        };

        Thread thread1 = new Thread(printNumbers);
        Thread thread2 = new Thread(printLetters);

        thread1.start();
        thread2.start();
    }
}

Example 3: Parallel Processing in JavaScript

function printNumbers() {
    for (let i = 0; i < 5; i++) {
        console.log(i);
    }
}

function printLetters() {
    for (let letter of 'abcde') {
        console.log(letter);
    }
}

// Using setTimeout to simulate asynchronous behavior
setTimeout(printNumbers, 0);
setTimeout(printLetters, 0);

Common Questions and Answers

1. What is the difference between concurrency and parallelism?
   Concurrency is about dealing with multiple tasks at once, while parallelism is about doing multiple tasks simultaneously.
2. Why is parallel processing important?
   It allows for faster execution of tasks by utilizing multiple processors or cores, improving performance and efficiency.
3. Can all tasks be parallelized?
   No, some tasks are inherently sequential and cannot be easily divided into parallel tasks.

Troubleshooting Common Issues

If your threads aren't running as expected, ensure they are properly started and joined. Also, check for any shared resource conflicts.

Remember, practice makes perfect! Try modifying the examples to deepen your understanding.

Try It Yourself Challenges

• Create a program that uses threads to calculate the sum of two arrays concurrently.
• Experiment with different numbers of threads and observe how it affects performance.

For further reading, check out the Python threading documentation or the Java concurrency tutorial.