In parallel computing, speedup refers to how much a parallel algorithm is faster than a corresponding sequential algorithm. Parallel computing is the simultaneous execution of the same task (split up and specially adapted) on multiple processors in order to obtain faster results. ... In computer science, a parallel algorithm, as opposed to a traditional serial algorithm, is one which can be executed a piece at a time in many different processing devices, and then put back together again at the end to get the correct result. ... Flowcharts are often used to represent algorithms. ...

It is defined by the following formula:

where:

• p is the number of processors
• T₁ is the execution time of the sequential algorithm
• T_p is the execution time of the parallel algorithm with p processors

Linear speedup or ideal speedup is obtained when . When running an algorithm with linear speedup, doubling the number of processors doubles the speed, which is usually considered very good scalability. A CPU The exact term processor is a sub-system of a data processing system which processes received information after it has been encoded into data by the input sub-system. ... Flowcharts are often used to represent algorithms. ... In computer science, a parallel algorithm, as opposed to a traditional serial algorithm, is one which can be executed a piece at a time in many different processing devices, and then put back together again at the end to get the correct result. ... A CPU The exact term processor is a sub-system of a data processing system which processes received information after it has been encoded into data by the input sub-system. ... In telecommunications and software engineering, scalability indicates the capability of a system to increase performance under an increased load when resources (typically hardware) are added. ...

Efficiency is a performance metric defined as S_p/p. It is a value, typically between zero and one, estimating how well-utilized the processors are in solving the problem, compared to how much effort is wasted in communication and synchronization. Algorithms with linear speedup and algorithms running on a single processor have an efficiency of 1, while many difficult-to-parallelize algorithms have efficiency such as 1/log p that approaches zero as the number of processors increases.

See also

• Amdahl's law