multicore-and-parallel-processors

Speed: If a task can be divided into subtasks that can be executed simultaneously, the total execution time can be reduced

Limit on maximum speed: Even with an infinite number of processors, there is a limit to the maximum speed improvement that can be made using parallel processing if a part of the program can’t be parallelised

Improved performance: Simultaneous computation can take place on different data subsets (this would be used in machine learning, data mining and scientific computing)

Complex programming: It is harder to write code for parallel processing than serial processing. Tasks have to be synchronised and data shared correctly

Better resource utilisation: Parallel processing allows for better use of computer resources as multi-core or multiple processors can be used more effectively

Debugging difficulty: It is more difficult to debug a parallel program than a serial program due to the precise timing of specific events 

Problem solving: Problems which are large and complex (which lend themselves to parallel processing) can be solved more easily

Communication between processors: Communication between processors can take significant time and resources, potentially outweighing the benefits of using parallel processing

Real-Time applications: Real-time applications including graphics rendering are more feasible and will benefit significantly

Limited applicability: Not all tasks can be run in parallel and must be executed serially