What is Parallel Algorithm?
A parallel algorithm is a type of algorithm that is designed to be executed concurrently by multiple processors or computers. The goal of a parallel algorithm is to take advantage of the processing power of multiple CPUs or computers to solve a problem faster than a single processor or computer could.
Parallel algorithms can be classified into two main categories:
- Data parallel algorithms: These algorithms operate on large data sets and divide the data into smaller chunks that can be processed concurrently by multiple processors or computers.
- Task parallel algorithms: These algorithms involve the concurrent execution of multiple independent tasks, which can be processed in parallel by multiple processors or computers.
Parallel algorithms can be implemented using a variety of programming languages and frameworks, including MPI (Message Passing Interface), OpenMP (Open Multiprocessing), and CUDA (Compute Unified Device Architecture).
Parallel algorithms are used in a wide range of applications, including scientific computing, data analysis, and machine learning. They are particularly well-suited for problems that require a large amount of computational power, such as data-intensive tasks or simulations.
An algorithm’s analysis enables us to decide whether or not it is useful. An algorithm is often evaluated based on how long it takes to execute (Time Complexity) and how much space it takes up (Space Complexity).
Storage space is no longer an issue because sophisticated memory devices are now affordable. As a result, space complexity is not given as much weight.
The purpose of parallel algorithms is to increase a computer’s processing speed. The following parameters are typically taken into account while examining a parallel algorithm:
- the total number of processors used,
- the execution time,
- and the overall cost.