Next Difference between Grid computing and Cluster computing. Recommended Articles. Article Contributed By :. Easy Normal Medium Hard Expert. Writing code in comment? Please use ide. Load Comments. What's New. In distributed memory systems, memory is divided among the processors. There are multiple advantages to parallel computing. As there are multiple processors working simultaneously, it increases the CPU utilization and improves the performance.
Moreover, failure in one processor does not affect the functionality of other processors. Therefore, parallel computing provides reliability. On the other hand, increasing processors is costly. Furthermore, if one processor requires instructions of another, the processor might cause latency. Distributed computing divides a single task between multiple computers. Each computer can communicate with others via the network. All computers work together to achieve a common goal. Thus, they all work as a single entity.
A computer in the distributed system is a node while a collection of nodes is a cluster. There are multiple advantages of using distributed computing. It allows scalability and makes it easier to share resources easily. It also helps to perform computation tasks efficiently.
On the other hand, it is difficult to develop distributed systems. Moreover, there can be network issues. Here we will discuss two computation types: parallel computing and distributed computing. The difference between Parallel Computing and Distributed Computing has been concisely highlighted in the table below.
Parallel computing also known as parallel processing , in simple terms, is a system where several processes compute parallelly. Hence parallel computing was introduced. Here, a single problem or process is divided into many smaller, discrete problems which are further broken down into instructions.
Every instruction is assigned a processor each to complete the task. These multiple processors are communicating with each other via a shared memory space and executing all the instructions simultaneously. The results of the execution of each task are finally combined for a single output of the overall problem. Parallel computing is housed in a single data centre, where several processors are installed, problems to compute are distributed in small chunks by the server.
They are then executed simultaneously on each server. The importance of parallel computing grows exponentially with the increasing usage of multicore processors. Parallel computing is used in areas of fields where massive computation or processing power is required and complex calculations are required. Parallel computation saves time.
The downside to parallel computing is that it might be expensive at times to increase the number of processors. Also, latency may occur as while instruction is executed at a processor it may be needed by another processor. Parallel computation can be classified into bit-level, instructional level, super word-level parallelism, data and task parallelism. Complex problems cannot be solved by using a single processor. So with this case of computing, a single problem can be divided into multiple tasks and distributed to many computers.
Distributed computing follows the same principle as parallel computing does. These computers communicate with each other by passing messages through the network.
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