Concurrency
Concurrency relates to an application that is processing more than one task at the same time. Concurrency is an approach that is used for decreasing the response time of the system by using the single processing unit. Concurrency is creates the illusion of parallelism, however actually the chunks of a task aren’t parallelly processed, but inside the application, there are more than one task is being processed at a time. It doesn’t fully end one task before it begins ensuing.
Concurrency is when two tasks overlap in execution. It could be a situation where an application is progressing on more than one task at the same time. We can understand it diagrammatically; multiple tasks are making progress at the same time, as follows −
Levels of Concurrency
In this section, we will discuss the three important levels of concurrency in terms of programming −
a. Low-Level Concurrency
In this level of concurrency, there is explicit use of atomic operations. We cannot use such kind of concurrency for application building, as it is very error-prone and difficult to debug. Even Python does not support such kind of concurrency.
b. Mid-Level Concurrency
In this concurrency, there is no use of explicit atomic operations. It uses the explicit locks. Python and other programming languages support such kind of concurrency. Mostly application programmers use this concurrency.
c. High-Level Concurrency
In this concurrency, neither explicit atomic operations nor explicit locks are used. Python has concurrent.futures module to support such kind of concurrency.
Properties of Concurrent Systems
For a program or concurrent system to be correct, some properties must be satisfied by it. Properties related to the termination of system are as follows −
a. Correctness property
The correctness property means that the program or the system must provide the desired correct answer. To keep it simple, we can say that the system must map the starting program state to final state correctly.
b. Safety property
The safety property means that the program or the system must remain in a “good” or “safe” state and never does anything “bad”.
c. Liveness property
This property means that a program or system must “make progress” and it would reach at some desirable state.
d. Actors of concurrent systems
This is one common property of concurrent system in which there can be multiple processes and threads, which run at the same time to make progress on their own tasks. These processes and threads are called actors of the concurrent system.
e. Resources of Concurrent Systems
The actors must utilize the resources such as memory, disk, printer etc. in order to perform their tasks.
f. Certain set of rules
Every concurrent system must possess a set of rules to define the kind of tasks to be performed by the actors and the timing for each. The tasks could be acquiring of locks, memory sharing, modifying the state, etc.
Parallelism
Parallelism is related to an application where tasks are divided into smaller sub-tasks that are processed seemingly simultaneously or parallel. It is used to increase the throughput and computational speed of the system by using multiple processors. It enables single sequential CPUs to do lot of things “seemingly” simultaneously.
Parallelism may be defined as the art of splitting the tasks into subtasks that can be processed simultaneously. It is opposite to the concurrency, as discussed above, in which two or more events are happening at the same time. We can understand it diagrammatically; a task is broken into a number of subtasks that can be processed in parallel, as follows −
To get more idea about the distinction between concurrency and parallelism, consider the following points −
Concurrent but not parallel
An application can be concurrent but not parallel means that it processes more than one task at the same time but the tasks are not broken down into subtasks.
Parallel but not concurrent
An application can be parallel but not concurrent means that it only works on one task at a time and the tasks broken down into subtasks can be processed in parallel.
Neither parallel nor concurrent
An application can be neither parallel nor concurrent. This means that it works on only one task at a time and the task is never broken into subtasks.
Both parallel and concurrent
An application can be both parallel and concurrent means that it both works on multiple tasks at a time and the task is broken into subtasks for executing them in parallel.
Necessity of Parallelism
We can achieve parallelism by distributing the subtasks among different cores of single CPU or among multiple computers connected within a network.
Consider the following important points to understand why it is necessary to achieve parallelism −
Efficient code execution
With the help of parallelism, we can run our code efficiently. It will save our time because the same code in parts is running in parallel.
Faster than sequential computing
Sequential computing is constrained by physical and practical factors due to which it is not possible to get faster computing results. On the other hand, this issue is solved by parallel computing and gives us faster computing results than sequential computing.
Less execution time
Parallel processing reduces the execution time of program code.
If we talk about real life example of parallelism, the graphics card of our computer is the example that highlights the true power of parallel processing because it has hundreds of individual processing cores that work independently and can do the execution at the same time. Due to this reason, we are able to run high-end applications and games as well.
Difference Between Concurrency and Parallelism
Concurrency | Parallelism |
Concurrency is the task of running and managing the multiple computations at the same time. | While parallelism is the task of running multiple computations simultaneously. |
Concurrency is achieved through the interleaving operation of processes on the central processing unit(CPU) or in other words by the context switching. | While it is achieved by through multiple central processing units(CPUs). |
Concurrency can be done by using a single processing unit. | While this can’t be done by using a single processing unit. it needs multiple processing units. |
Concurrency is the non-deterministic control flow approach. | Parallelism is deterministic control flow approach. |
In concurrency debugging is very hard. | While in this debugging is also hard but simple than concurrency. |
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