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Amdahl's law is often conflated with the law of diminishing returns, whereas only a special case of applying Amdahl's law demonstrates law of diminishing returns. If one picks optimally (in terms of the achieved speedup) what is to be improved, then one will see monotonically decreasing improvements as one improves.
English: SVG Graph illustrating Amdahl's law – A plot of Amdahl’s law with logarithmic x-axis and linear y-axis. The speed-up of a program from parallelization is limited by how much of the program can be parallelized.
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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 ...
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 ...
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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's Law reveals a limitation in, for example, the ability of multiple cores to reduce the time it takes for a computer to boot to its operating system and be ready for use. Assuming the boot process was mostly parallel, quadrupling computing power on a system that took one minute to load might reduce the boot time to just over fifteen seconds.