Zeigler, "How to apply Amdahl's law
to multithreaded multicore processors," Journal of Parallel and Distributed Computing, vol.
 is one of the few fundamental laws of computing that contribute to systems' performance enhancement.
RELATED ARTICLE: Promise and Limits of Amdahl's law
and Moore's Law
This equation Amdahl's law
on basis of computing-centric system which never takes into account the potential cost of data preparation.
One of these programmers even wrote a paper explaining why Amdahl's law
We propose a load balancing technique that can overcome the performance limitations of Amdahl's law
. In order to reduce the idle time caused by the data dependency in the second workload, we distribute the data-independent first workload unevenly across the cores and keep the data dependency in the second workload.
The reason for this lies in Amdahl's law
. The most important inherently sequential part in our program is the quantization control.
We will start with Amdahl's Law
 which in its simplest form says that
The distrust of an achievable large speedup from the massively parallel system is raised mainly from Amdahl's law
. Amdahl's law
indicates that the maximum speedup, even on a parallel system with an infinite number of processors, cannot exceed 1/k, where k is the fraction of operations that cannot be executed in parallel.
We found that we had ignored, at our peril, Amdahl's law
. According to an argument presented by Gene Amdahl in 1967, the speedup possible with multiple processors is speedup = (s + p)/(s + p/N) where s is the time spent running the serial portion of the program, p is the time spent on the parallel portion, and N is the number of processors.
In his technical note, "Reevaluating Amdahl's Law
," in the May 1988 Communications (pp.
The harmonic mean is in accord with Amdahl's law
, which, when applied to this example, asserts that making the second program infinitely fast will only have the total time used by both programs.