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Amdahl's law applies only to the cases where the problem size is fixed. In practice, as more computing resources become available, they tend to get used on larger problems (larger datasets), and the time spent in the parallelizable part often grows much faster than the inherently serial work.
Gustafson's law addresses the shortcomings of Amdahl's law, which is based on the assumption of a fixed problem size, that is of an execution workload that does not change with respect to the improvement of the resources. Gustafson's law instead proposes that programmers tend to increase the size of problems to fully exploit the computing power ...
All three speedup models, Sun–Ni, Gustafson, and Amdahl, provide a metric to analyze speedup for parallel computing. Amdahl’s law focuses on the time reduction for a given fixed-size problem. Amdahl’s law states that the sequential portion of the problem (algorithm) limits the total speedup that can be achieved as system resources increase.
Karp and Flatt hoped to correct this by proposing this metric. This metric addresses the inadequacies of the other laws and quantities used to measure the parallelization of computer code. In particular, Amdahl's law does not take into account load balancing issues, nor does it take overhead into consideration. Using the serial fraction as a ...
More technically, it is the improvement in speed of execution of a task executed on two similar architectures with different resources. The notion of speedup was established by Amdahl's law, which was particularly focused on parallel processing. However, speedup can be used more generally to show the effect on performance after any resource ...
Amdahl, Gene: Pioneer of mainframe computing; designed IBM 704; chief architect of IBM System/360. [4] [5] Formulated Amdahl's law; also worked on IBM 709 and IBM 7030 Stretch. [6] 1939 Atanasoff, John: Built the first electronic digital computer, the Atanasoff–Berry Computer, though it was neither programmable nor Turing-complete. 1822, 1837
The maximum potential speedup of an overall system can be calculated by Amdahl's law. [14] Amdahl's Law indicates that optimal performance improvement is achieved by balancing enhancements to both parallelizable and non-parallelizable components of a task. Furthermore, it reveals that increasing the number of processors yields diminishing ...
Amdahl's Law, commonly used to calculate performance gains of throwing more CPUs at a problem, can be applied more generally to improving latency – that is, improving a portion of a system which is already fairly inconsequential (with respect to latency) will result in minimal improvement in the overall performance.