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.
== Definition ==  
== Definition ==  
== Types of speedup ==
== Types of speedup ==

Revision as of 23:32, 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.

Definition

Types of speedup

Scaled speedup

Superlinear speedup

Conclusion

References