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
Tp is the execution time of the parallel algorithm with p processors
Linear speedup or ideal speedup is obtained when Sp = p . 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 Sp/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.
It is defined by the following formula:
where:
p is the number of processors
T1 is the execution time of the sequential algorithm
Tp is the execution time of the parallel algorithm with p processors
Linear speedup or ideal speedup is obtained when Sp = p . 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 Sp/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.
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