Parallel computing is a type of computing that involves carrying out multiple computations simultaneously, using multiple processing units or cores, in order to improve the overall speed and efficiency of the process. The motivation behind parallelism stems from the need to process large amounts of data and complex computations in a timely and efficient manner. As the size of datasets and complexity of algorithms continue to grow, traditional sequential computing approaches are becoming increasingly inadequate, leading to longer processing times and decreased productivity.
Parallel computing offers a solution to this problem by allowing multiple computations to be carried out simultaneously, thereby reducing processing times and increasing productivity. It enables large and complex computations to be broken down into smaller, more manageable tasks that can be executed concurrently. This not only saves time, but also allows for more efficient use of computing resources, as multiple processors or cores can be utilized simultaneously.
In addition to improving speed and efficiency, parallel computing also enables the development of more complex and sophisticated algorithms, as it allows for the processing of larger datasets and more intricate computations. This has important implications for a wide range of fields, including scientific research, data analysis, machine learning, and more.
Overall, the motivation behind parallel computing is to address the limitations of traditional sequential computing, by enabling faster and more efficient processing of complex computations and large datasets.