The development of parallel programming created the need for performance metrics in order to decide whether its use is convenient or not. Indeed, the focus of parallel computing is to solve large problems in a relatively short period of time. The factors contributing to this objective are, for example, the type of hardware used, the degree of parallelism of the problem, and the parallel programming model adopted. To facilitate this, the analysis of basic concepts was introduced, which compares the parallel algorithm obtained from the original sequence.

The performance is achieved by analyzing and quantifying the number of threads and/or the number of processes used. To analyze this, let's introduce a few performance indexes:

• Speedup
• Efficiency
• Scaling

The limitations of parallel computation are introduced by Amdahl's law. To evaluate the degree of efficiency of the parallelization of a sequential algorithm, we have Gustafson's law.