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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.
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Jetzt kostenlos anmeldenDive 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.
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.
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.
Concurrency: Concurrency occurs when two or more tasks start, run, and complete in overlapping time periods. It doesn't necessarily mean they'll be running at the same instant. For example, multitasking on a single-core machine.
Imagine you're preparing a meal. You'll be working on numerous tasks like chopping vegetables, marinating the chicken, boiling rice and so on. These tasks aren't being performed at the same exact moment - you might chop vegetables while the chicken is marinating. This act of hopping from one task to another is concurrency.
Parallelism: Parallelism, on the other hand, occurs when two or more tasks run at the same time (simultaneously). They start, run, and complete in parallel.
In your PC, when your processor has more than one core, it is capable of running multiple threads at the same time. Each processor core can be working on a different task. This is a form of parallelism.
| Concept | Instance of Correspondence |
| Concurrency | Starting, running, and completing tasks overlap in time. |
| Parallelism | Tasks run simultaneously. |
The primary difference between concurrency and parallelism is related to the actual and simultaneous running of tasks. In Concurrency, tasks appear to run at the same time, but in reality, they may not be running simultaneously, mainly in single-core CPU. In contrast, tasks truly run at the same time in parallelism, principally in multicore CPU.
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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.
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.
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.
Single-core (Concurrency): In single-core computers, threads of a program aren't genuinely running at the same time; instead, the operating system quickly switches between threads giving an illusion of simultaneous execution.
To illustrate, when a person is cooking (the program), they manage various tasks such as chopping vegetables, heating a pan, and so on (different threads). There's only one person (single-core), but by rapidly switching between tasks, the process seems like everything is getting done at once, and that's concurrency.
Multi-core (Parallelism): With multi-core computers, different threads can genuinely run at the same time because each thread runs on a separate core.
Assume now there is a team of chefs (multi-core) and each one is assigned a particular task. Here, various tasks get done genuinely at the same time, and this represents parallelism.
| Process | Example |
| Concurrency | Single cook managing multiple tasks |
| Parallelism | Multiple chefs carrying out different tasks |
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.
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.
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.
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.
Concurrency is about dealing with a lot of things at once. It refers to the notion that an application is making progress on more than one task, at virtually the same time. Emphasising the notion of 'virtually', it's due to the simple fact that even on single-core CPUs, time-slicing, a method performed by the CPU via interrupt mechanism, enables the single-core processor to distribute its processing time to the tasks so that they all appear to be running at the same time, hence giving the illusion of simultaneity.
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.
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.YOUR SCORE
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SIGNUP SIGNUPFlashcards in Concurrency Vs Parallelism12
Start learningWhat is concurrency in computer science terms?
Concurrency occurs when two or more tasks start, run, and complete in overlapping time periods. They may not run at the exact same moment. For example, multitasking on a single-core machine.
What is parallelism in the context of computing?
Parallelism happens when two or more tasks run at the same exact time (simultaneously). They start, run, and complete in parallel. This often requires a multi-core processor.
What's the primary difference between concurrency and parallelism?
The primary difference is related to the actual and simultaneous running of tasks. Tasks appear to run simultaneously in concurrency, but may not actually. In contrast, tasks truly run at the same time in parallelism.
What is the concurrency level in a multithreaded system?
The concurrency level in a multithreaded system is calculated with the formula: Total Time For All Processors divided by Longest Path Wall-clock Time. In Perfect parallelism, it's equal to the number of threads.
What is the difference between concurrency and parallelism in the context of computer science?
In single-core computers, concurrency gives the illusion of simultaneous execution by rapidly switching between threads. In contrast, parallelism in multi-core computers allows different threads to genuinely run simultaneously, as each thread runs on a separate core.
How are the concepts of concurrency and parallelism illustrated with the example of cooks?
Concurrency is like a single cook managing multiple tasks by switching between them rapidly, creating the illusion of simultaneous work. Parallelism is like multiple chefs each carrying out different tasks genuinely at the same time.
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