Concurrency Vs Parallelism

Dive into the intricate world of computer science with an in-depth exploration of Concurrency Vs Parallelism. This comprehensive guide reveals their definitions, applications in computer programming languages like Java and Python, and explores their relation with multithreading. It also elucidates the practical coding implications of these concepts, focusing on the role of synchronisation. Get ready to deepen your understanding and navigate the complexities of Concurrency Vs Parallelism.

Concurrency Vs Parallelism: An Overview

In the landscape of computer science, two significant concepts that determine the speed and efficiency of programs are concurrency and parallelism. Both elements come into play when tasks need to be processed simultaneously or in overlapping time frames. However, it's crucial to understand the unique qualities of each and how they can impact your computational work.

Definition of Concurrency and Parallelism

Often mistaken for each other, concurrency and parallelism represent different types of handling multiple tasks at once. Yet, they carry unique features and implications towards performance and resource allocation.

To see these concepts visually, consider the following table:

Concept	Instance of Correspondence
Concurrency	Starting, running, and completing tasks overlap in time.
Parallelism	Tasks run simultaneously.

In multithreaded systems, threads can be executed concurrently or in parallel. Use the formula below written in LaTeX: \[ Concurrency Level = \frac{Total Time For All Processors}{Longest Path Wall-clock Time} \] This formula helps to calculate the concurrency level of a particular system. In the case of perfect parallelism, the concurrency level would be equal to the number of threads. Here is a simple code written in python to illustrate concurrency:

import threading
def thread_function():
    for i in range(10):
        print("Thread: {}".format(i))

if __name__ == "__main__":
    for i in range(5):
        threading.Thread(target=thread_function).start()

In the code above, all threads run concurrently rather than parallelly. Understanding these differences can significantly affect how you design and implement programs, especially in a real-time system.

Concurrency vs Parallelism in Computer Programming Languages

In Computer Science, both concurrency and parallelism, concepts are applied across various programming languages to enhance the efficiency of executing tasks. Popular languages like Java and Python harness these vital principles to optimize computational speed and resource allocation. The treatment of these principles in different languages gives us a fresh perspective on our understanding of concurrency and parallelism.

Concurrency Vs Parallelism Example

It's often helpful to consider concrete examples to understand these abstract concepts better. The example of a multi-threaded application running on a single-core versus a multi-core processor helps illustrate the principles of concurrency and parallelism.

This comparison can be tabulated as:

Process	Example
Concurrency	Single cook managing multiple tasks
Parallelism	Multiple chefs carrying out different tasks

Concurrency vs Parallelism in Java

In terms of programming languages, Java provides excellent frameworks to handle both concurrency and parallelism. Here, multiple threads are typically used to achieve concurrency. For instance, Java's 'ExecutorService' creates a pool of threads for executing tasks concurrently.

Here's how to create a thread in Java:

public class Main {
  public static void main(String[] args) {
    Thread thread = new Thread() {
      public void run() {
        System.out.println("Thread Running");
      }
    };
    thread.start();
  }
}

Parallelism in Java is catered to multi-core processors where the 'Fork/Join' framework is used to execute tasks in parallel for load balancing.

Concurrency vs Parallelism Python

Python, another popular language, also caters to both concurrency and parallelism. The 'threading' library in Python allows concurrency where multiple threads are created and managed by the Python interpreter. Here's an example:

import threading

def print_numbers():
    for i in range(10):
        print(i)

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

thread1 = threading.Thread(target=print_numbers)
thread2 = threading.Thread(target=print_letters)

thread1.start()
thread2.start()

For parallelism, Python has the 'multiprocessing' module that utilises multiple cores of the CPU, allowing simultaneous execution of processes. Understanding and correctly implementing these concepts can significantly influence the performance and efficiency of your programs.

Deep Dive: Concurrency Vs Parallelism Vs Multithreading

In the realm of computer science, periodic confusion arises regarding terms such as concurrency, parallelism, and multithreading. They share similarities but serve different purposes when it comes to optimising computing efficiency.

Difference Between Concurrency and Parallelism

An understanding of the distinct differences between concurrency and parallelism is paramount to visualising how tasks are organised and processed. It starts with comprehending the basics of task execution.

On the other hand, parallelism involves executing multiple tasks or several parts of a unique task at the same time. It is, in essence, a subset of concurrency, but it specifically refers to the simultaneous execution of computations or processes. In a nutshell, the primary differences between the two can be summarised as follows:

• Concurrency focuses on managing multiple tasks at once, not necessarily implying that they're running simultaneously.
• Parallelism refers to the simultaneous execution of multiple tasks or distributing different parts of a specific task amongst different processors.

Synchronization in Concurrency and Parallelism

Regardless of whether tasks are running concurrently or in parallel, there is a need for synchronization when sharing resources. When tasks need to share any resources like memory, database connections, or even hardware devices, they are said to be synchronised.

Typically, obstacles arise when multiple tasks need to utilise shared resources, which can result in conflicting operations, termed as "race conditions". Synchronization techniques help to prevent these issues. In concurrent programming, lock-based synchronization is commonly used. Each shared resource has a corresponding lock. When a task wants to access the resource, it must first obtain the lock. If another task is already holding the lock, the task waits until the lock is available. On the contrary, parallel programming often adopts the principle of avoiding sharing state – the MapReduce programming model for distributed computation works on this principle. The goal is to divide the task into completely independent subtasks that can be executed in parallel without requiring synchronization.

Coding Implications of Concurrency Vs Parallelism

When writing computer programs, it is essential to consider the constraints and abilities of both concurrency and parallelism. The choice often depends on various factors such as the nature of tasks, system architecture, and intended responsiveness of the application. In a concurrent application, you often deal with a lot of tasks at once, and there are many issues of communication, synchronization, data sharing and coordination to consider. The primary issues in concurrent programming include race conditions, deadlocks and starvation. These can be managed through different techniques like locks, semaphores and monitors.

public class ConcurrencyExample {
  private static final int POOL_SIZE = 5;
  public static void main(String[] args) {
    ExecutorService pool = Executors.newFixedThreadPool(POOL_SIZE);
    for (int threadCnt = 0; threadCnt < POOL_SIZE; threadCnt++) {
      Runnable runnable = new ConcurrencyExample().new Task(threadCnt);
      pool.execute(runnable);
    }
    pool.shutdown();
  }
}

Parallel programming carries its unique set of challenges, including task partitioning, load balancing, and scalability concerns. These can be managed using techniques such as parallel algorithms, atomic operations and thread safety.

from multiprocessing import Pool

def f(x):
    return x * x

if __name__ == '__main__':
    with Pool(5) as p:
        print(p.map(f, [1, 2, 3, 4 ,5]))

In summary, both concurrency and parallelism have profound implications on how you structure your code and design your application. Whether you use them and how you use them can drastically affect your application's performance and responsiveness.