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In computer science, concurrency (꽁머니) means the power of parallel elements or independent units of a system, technique, or application to be executed efficiently or out-of-place, without disturbing the result. Concurrent programming is a subset of parallelism, and the two are often used interchangeably. Because concurrency means “the ability to run multiple programs on a machine”, programmers must be careful to properly understand the limitations and advantages of concurrency. Concurrent programming helps avoid the overhead associated with multiple program execution, as well as all the wasted effort and potentially damaging programming errors that come from using multiple threads at the same time. It also makes programming easier by letting programmers coordinate and use threads in the most efficient way possible.

Concurrent programming tools allow programmers to create tasks on the server and then communicate with them on the client side. These tools make it easier to create concurrency through the use of threads. The more concurrency that is created, the more memory space is used, so the overall memory usage will increase as well. The more concurrency, the harder it is to maintain a stable performance level because there are more opportunities for errors.

Concurrent systems must support a wide variety of independent operations that rely on each other, in order for them to function correctly. concurrency has to do with concurrency, as well as the abstractions used to create those functions. Concurrent systems must also provide an accurate model of concurrency, including message ordering and priority rules. Programmers interested in concurrency should seek concurrency abstractions that allow them to model multiple concurrently running tasks in a generic manner. Akka, Cilk, and Playfair are three popular concurrency abstractions.

Concurrent programming does not have to involve multiple CPU’s. It can only be done effectively with one or two machines, and these machines should be able to divide their processing power efficiently. Concurrent programming is not only limited to the execution of tasks on a single machine; it can also have significant consequences for the performance of multiple tasks on one machine. Distributed computing is a form of concurrency where computers share resources, such as memory and CPU time, on multiple machines. The most common form of distributed computing is cloud computing in which servers handle tasks performed by multiple machines, rather than a single machine.

Concurrent programming can be implemented using parallelism control languages, such as ML, MATLAB, Python, R, or Perl. Parallelism control languages help the programmer to isolate and manage the execution of various tasks on a parallel machine. In the past, parallelism control programming was difficult because programmers had to assume that each process in a parallel program would complete in the same amount of time. However, improvements to the Concurrent Programming language and tools have resulted in much easier parallelism control.

Parallelism also leads to parallelism. The programmer needs to decide what quantity of parallelism is desirable for a given system. The desired concurrency can be defined in terms of throughput, average time, and costs. With concurrency, multiple processes execute simultaneously. Concurrent systems usually have a shared-memory workspace and communicate over a network.