What is the difference between parallelism and concurrency

This means that it works on only one task at a time, and the task is never broken down into subtasks for parallel execution. This could be the case for small command line applications where it only has a single job which is too small to make sense to parallelize. The first is simple parallel concurrent execution.

This is what happens if an application starts up multiple threads which are then executed on multiple CPUs. The second way is that the application both works on multiple tasks concurrently, and also breaks each task down into subtasks for parallel execution. However, some of the benefits of concurrency and parallelism may be lost in this scenario, as the CPUs in the computer are already kept reasonably busy with either concurrency or parallelism alone.

Combining it may lead to only a small performance gain or even performance loss. Make sure you analyze and measure before you adopt a concurrent parallel model blindly. Tutorials About RSS. Java Concurrency. Concurrency vs. Concurrency vs Parallelism Tutorial Video If you prefer video, I have a video version of this tutorial here: Concurrency vs Parallelism Tutorial Video Concurrency Concurrency means that an application is making progress on more than one task - at the same time or at least seemingly at the same time concurrently.

This is illustrated in the diagram below: Parallel Execution Parallel execution is when a computer has more than one CPU or CPU core, and makes progress on more than one task simultaneously. Parallel execution is illustrated below: Parallel Concurrent Execution It is possible to have parallel concurrent execution, where threads are distributed among multiple CPUs.

Parallelism The term parallelism means that an application splits its tasks up into smaller subtasks which can be processed in parallel, for instance on multiple CPUs at the exact same time.

You need to pause the video, apply what been said in code then continue watching. That's concurrency. Now you're a professional programmer. And you enjoy listening to calm music while coding. That's Parallelism. Concurrency is about dealing with lots of things at once. Parallelism is about doing lots of things at once. Assume that an organization organizes a chess tournament where 10 players with equal chess playing skills will challenge a professional champion chess player.

And since chess is a game thus organizers have to conduct 10 games in time efficient manner so that they can finish the whole event as quickly as possible. In other words, they decided to conduct the games sequentially. So if one game takes 10 mins to complete then 10 games will take mins, also assume that transition from one game to other takes 6 secs then for 10 games it will be 54 secs approx. Now the event is progressing in parallel in these two sets i. However within the group the professional player with take one player at a time i.

So the whole event involving two such parallel running group will approximately complete in NOTE: in the above scenario if you replace 10 players with 10 similar jobs and two professional players with two CPU cores then again the following ordering will remain true:. NOTE: this order might change for other scenarios as this ordering highly depends on inter-dependency of jobs, communication needs between jobs and transition overhead between jobs.

Parallelism is simultaneous execution of processes on a multiple cores per CPU or multiple CPUs on a single motherboard. Processes are interleaved.

They solve different problems. Concurrency solves the problem of having scarce CPU resources and many tasks. So, you create threads or independent paths of execution through code in order to share time on the scarce resource.

Up until recently, concurrency has dominated the discussion because of CPU availability. Parallelism solves the problem of finding enough tasks and appropriate tasks ones that can be split apart correctly and distributing them over plentiful CPU resources. Parallelism has always been around of course, but it's coming to the forefront because multi-core processors are so cheap. Concurrent programming execution has 2 types : non-parallel concurrent programming and parallel concurrent programming also known as parallelism.

The key difference is that to the human eye, threads in non-parallel concurrency appear to run at the same time but in reality they don't. In non - parallel concurrency threads rapidly switch and take turns to use the processor through time-slicing. While in parallelism there are multiple processors available so, multiple threads can run on different processors at the same time. Reference: Introduction to Concurrency in Programming Languages.

In other words, concurrency is sharing time to complete a job, it MAY take up the same time to complete its job but at least it gets started early. Important thing is , jobs can be sliced into smaller jobs, which allows interleaving. Keep in mind, if the resources are shared, pure parallelism cannot be achieved, but this is where concurrency would have it's best practical use, taking up another job that doesn't need that resource.

I'm going to offer an answer that conflicts a bit with some of the popular answers here. In my opinion, concurrency is a general term that includes parallelism. Concurrency applies to any situation where distinct tasks or units of work overlap in time. The raison d'etre of parallelism is speeding up software that can benefit from multiple physical compute resources. The other major concept that fits under concurrency is interactivity. Interactivity applies when the overlapping of tasks is observable from the outside world.

The raison d'etre of interactivity is making software that is responsive to real-world entities like users, network peers, hardware peripherals, etc. Parallelism and interactivity are almost entirely independent dimension of concurrency. For a particular project developers might care about either, both or neither.

They tend to get conflated, not least because the abomination that is threads gives a reasonably convenient primitive to do both. Parallelism exists at very small scales e. Pressure on software developers to expose more thread-level parallelism has increased in recent years, because of the growth of multicore processors. Parallelism is intimately connected to the notion of dependence.

Dependences limit the extent to which parallelism can be achieved; two tasks cannot be executed in parallel if one depends on the other Ignoring speculation.

There are lots of patterns and frameworks that programmers use to express parallelism: pipelines, task pools, aggregate operations on data structures "parallel arrays". The most basic and common way to do interactivity is with events i. For simple tasks events are great.

Trying to do more complex tasks with events gets into stack ripping a. When you get fed up with events you can try more exotic things like generators, coroutines a. For the love of reliable software, please don't use threads if what you're going for is interactivity.

I dislike Rob Pike's "concurrency is not parallelism; it's better" slogan. Concurrency is neither better nor worse than parallelism. It's like saying "control flow is better than data". In electronics serial and parallel represent a type of static topology, determining the actual behaviour of the circuit.

When there is no concurrency, parallelism is deterministic. In order to describe dynamic, time-related phenomena , we use the terms sequential and concurrent. For example, a certain outcome may be obtained via a certain sequence of tasks eg. When we are talking with someone, we are producing a sequence of words. However, in reality, many other processes occur in the same moment, and thus, concur to the actual result of a certain action.

If a lot of people is talking at the same time, concurrent talks may interfere with our sequence, but the outcomes of this interference are not known in advance. Concurrency introduces indeterminacy. An example of this is in digital communication. In a serial adapter , a digital message is temporally i. In a parallel adapter , this is divided also on parallel communication lines eg.

Let us image a game, with 9 children. If we dispose them as a chain, give a message at the first and receive it at the end, we would have a serial communication.

More words compose the message, consisting in a sequence of communication unities. This is a sequential process reproduced on a serial infrastructure. Now, let us image to divide the children in groups of 3.

We divide the phrase in three parts, give the first to the child of the line at our left, the second to the center line's child, etc. This is a sequential process reproduced on a parallel infrastructure still partially serialized although. In both cases, supposing there is a perfect communication between the children, the result is determined in advance.

If there are other persons that talk to the first child at the same time as you, then we will have concurrent processes. We do no know which process will be considered by the infrastructure, so the final outcome is non-determined in advance. Concurrency is an aspect of the problem domain —your code needs to handle multiple simultaneous or near simultaneous events. Parallelism, by contrast, is an aspect of the solution domain —you want to make your program run faster by processing different portions of the problem in parallel.

Some approaches are applicable to concurrency, some to parallelism, and some to both. Threads create two related but distinct phenomena: concurrency and parallelism.

Both are bittersweet, touching on the costs of threading as well as its benefits. Concurrency is the ability of two or more threads to execute in overlapping time periods. Parallelism is the ability to execute two or more threads simultaneously. Concurrency can occur without parallelism: for example, multitasking on a single processor system. Parallelism sometimes emphasized as true parallelism is a specific form of concurrency requiring multiple processors or a single processor capable of multiple engines of execution, such as a GPU.

With concurrency, multiple threads make forward progress, but not necessarily simultaneously. With parallelism, threads literally execute in parallel, allowing multithreaded programs to utilize multiple processors.

Concurrency is a programming pattern, a way of approaching problems. Parallelism is a hardware feature, achievable through concurrency. Both are useful. This explanation is consistent with the accepted answer. Actually the concepts are far simpler than we think. Don't think them as magic. Concurrency is about a period of time , while Parallelism is about exactly at the same time , simultaneously. Concurrency is the generalized form of parallelism. For example parallel program can also be called concurrent but reverse is not true.

Concurrent execution is possible on single processor multiple threads, managed by scheduler or thread-pool. Parallel execution is not possible on single processor but on multiple processors. One process per processor. Distributed computing is also a related topic and it can also be called concurrent computing but reverse is not true, like parallelism. For details read this research paper Concepts of Concurrent Programming. I really liked this graphical representation from another answer - I think it answers the question much better than a lot of the above answers.

Parallelism vs Concurrency When two threads are running in parallel, they are both running at the same time. For example, if we have two threads, A and B, then their parallel execution would look like this:. When two threads are running concurrently, their execution overlaps. Overlapping can happen in one of two ways: either the threads are executing at the same time i.

The simplest and most elegant way of understanding the two in my opinion is this. Concurrency allows interleaving of execution and so can give the illusion of parallelism. This means that a concurrent system can run your Youtube video alongside you writing up a document in Word, for example. The underlying OS, being a concurrent system, enables those tasks to interleave their execution. Because computers execute instructions so quickly, this gives the appearance of doing two things at once.

Similarly, the JavaScript Event Loop allows your scripts the chef to hand off tasks like HTTP requests and timeouts to the browser Web API rice cooker , allowing the script to execute other code portions while waiting for a response.

While the Web API acts as a separate thread where it can complete certain tasks outside the main thread's scope, your actual JavaScript code is still executed on a single thread concurrently. Parallelism describes the ability for independent parts of a program to be physically executed at the same time. A parallel application can distribute its tasks to independent processors such as different cores or threads of a CPU to be executed simultaneously.

You can think of a parallel execution model as multiple chefs individually each preparing a meal. These individual chefs may be preparing their dishes in a concurrent manner like the above or a sequential one; either way, the result is that rather than producing a single meal, the kitchen has prepared multiple meals over a unit of time. Modern browsers allow you to program parallelly by using Web Workers.

These spawn separate threads to execute JavaScript independently from the main thread. So we've established that multiple chefs can get a kitchen to produce multiple dishes in the same amount of time as a single dish from a kitchen with a single chef.

Modern hardware almost always has multiple threads, so why isn't all code run in parallel? If it takes one chef 10 minutes to prepare one stir-fry and five chefs 10 minutes to prepare five stir-fries, can five chefs produce one stir-fry in 2 minutes?

This is where parallel computation can get difficult. Tasks can speed up by distributing the workload onto multiple threads. However, this requires splitting up the workload in a way that can work independently and effectively. Think of how five chefs would prepare a single stir fry together:. Similarly, on the computing side, parallel programming solutions are generally harder to implement and debug. Depending on the task, they can sometimes even perform worse than serially run counterparts due to the various costs of overhead transferring data between threads, creating and destroying threads, synchronization of work, etc.

Directly into your inbox, for free. A program could be run on a multi core or uni core CPU. However if this program is run on a multi core CPU and can make use of more than one core than it has the capability for parallel processing, otherwise it cannot be used for parallel processing even on a multi core CPU.

Hi Lokesh, Thanks for bringing this point to notice. A blog about Java and its related technologies, the best practices, algorithms, interview questions, scripting languages, and Python. About Me. Contact Us. Privacy policy. Guest Posts. Secure Hash Algorithms. Best Way to Learn Java. How to Start New Blog.