An **Algorithm** is a series of Computational steps that take inputs from the user and after some Computational processing, gives out an output. A **Parallel Algorithm** is an Algorithm that is able to carry out several instructions simultaneously on different processing devices and then join all the individual results to bring up the final result.

**Parallelism **here is defined as the process of carrying out several sets of instructions simultaneously. It reduces the total time for Computation. Parallelism can be carried out by using **Parallel Computers,** an example is a computer that has many processors. Parallel Computers needs Parallel Algorithm, **Programming languages,** Compilers and an Operating System that can support multitasking.

The easy availability of computers and the development of the Internet have changed the way we store and process data. We are now living in a day and age where data is available in large quantities. Every day we come across large volumes of data that depend on **Complex Computing** and that too, in quick time.

Sometimes, we need to get data from like or interrelated events that occur simultaneously. This is the area in which we would need **Concurrent Processing** that can split a hard and complicated task and process it in multiple systems to bring up meaningful output in a quick time.

**Concurrent Processing** is an essential part of the Parallel Algorithm in places where the task to be carried out involves processing a very large amount of complex data. Examples include querying large databases, enormous calculations, testing aircraft, study of atomic and nuclear physics, biomedical analysis, economic planning, image processing, robotics, weather forecasting, web-based services, etc.

There are lots of features of Parallel Algorithm, some of them are:

**1. Speedup of an Algorithm:** The performance of a Parallel Algorithm can be found by calculating its speedup. Speedup in the Parallel Algorithm can be defined as the ratio of the worst-case execution time of the fastest known sequential algorithm for a particular problem to the worst-case execution time of the parallel algorithm.

**2. Number of Processors Used:** The number of processors used is an important factor when analyzing the efficiency of a parallel algorithm. The price of buying, maintaining and running computers are calculated beforehand. A large number of processors used by the algorithm to solve the problem makes it more costly but the obtained result is worth it.

**3. Asymptotic Analysis:** The complexity or efficiency of an algorithm is the number of steps executed by the algorithm to get the desired output. Asymptotic analysis is used to determine how complex an algorithm is in its theoretical analysis. In an asymptotic analysis, a large length of the input is used to calculate the complex function of the algorithm.

**4. Total Cost:** The total cost of a Parallel Algorithm is the product of time complexity and the number of processors used in that particular algorithm.

The following are key advantages of Parallel Programming that drive its use for developing computing solutions:

1. The main reason for parallel programming is to process code efficiently, since parallel programming reduces processing time, therefore allowing for the execution of applications in a smaller amount of time. As a result of executing code efficiently, Parallel Programming most times, increases with the problem size, making it able to solve larger problems. In general, Parallel Programming is a way of providing concurrency, especially for carrying out simultaneously multiple actions at the same time.

2. Parallel Programming goes above just the limits that are required by sequential computing, which is mostly restrained by physical and practical factors that restrict the ability to create faster sequential computers. For example, the speed of a sequential computer depends on how fast data travels in its hardware.

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