In parallel computing, speedup refers to how much a parallel algorithm is faster than a corresponding sequential algorithm.

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.

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