CSC/ECE 506 Spring 2012/4b rs: Difference between revisions
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== Introduction == | == Introduction == | ||
In parallel computing, speedup refers to how much a parallel algorithm is faster than a corresponding sequential algorithm. According to Amdahl's law the speedup of a program using multiple processors in parallel computing is limited by the time needed for the sequential fraction of the program. But this solves a fixed problem in the shortest possible period of time, rather than solving the largest possible problem (e.g., the most accurate possible approximation) in a fixed "reasonable" amount of time. To overcome these shortcomings, John L. Gustafson and his colleague Edwin H. Barsis described Gustafson's Law, which provides a counterpoint to Amdahl's law, which describes a limit on the speed-up that parallelization can provide, given a fixed data set size. | In parallel computing, speedup refers to how much a parallel algorithm is faster than a corresponding sequential algorithm. According to [http://en.wikipedia.org/wiki/Gustafson%27s_law#Derivation_of_Gustafson.27s_Law Amdahl's law] the speedup of a program using multiple processors in parallel computing is limited by the time needed for the sequential fraction of the program. But this solves a fixed problem in the shortest possible period of time, rather than solving the largest possible problem (e.g., the most accurate possible approximation) in a fixed "reasonable" amount of time. To overcome these shortcomings, John L. Gustafson and his colleague Edwin H. Barsis described [http://en.wikipedia.org/wiki/Gustafson%27s_law#Derivation_of_Gustafson.27s_Law Gustafson's Law], which provides a counterpoint to Amdahl's law, which describes a limit on the speed-up that parallelization can provide, given a fixed data set size. | ||
== Types of speedup == | == Types of speedup == | ||
Revision as of 23:35, 12 February 2012
The limits to speedup
Introduction
In parallel computing, speedup refers to how much a parallel algorithm is faster than a corresponding sequential algorithm. According to Amdahl's law the speedup of a program using multiple processors in parallel computing is limited by the time needed for the sequential fraction of the program. But this solves a fixed problem in the shortest possible period of time, rather than solving the largest possible problem (e.g., the most accurate possible approximation) in a fixed "reasonable" amount of time. To overcome these shortcomings, John L. Gustafson and his colleague Edwin H. Barsis described Gustafson's Law, which provides a counterpoint to Amdahl's law, which describes a limit on the speed-up that parallelization can provide, given a fixed data set size.