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In computer science, a parallel algorithm, as opposed to a traditional serial algorithm, is an algorithm which can do multiple operations in a given time. It has been a tradition of computer science to describe serial algorithms in abstract machine models, often the one known as random-access machine .
The sequential limits on parallel performance dictated by Amdahl's law also do not apply in the same way because data dependencies are implicitly handled by the programmable node interconnect. Therefore, systolic arrays are extremely good at artificial intelligence, image processing, pattern recognition, computer vision, and other tasks that ...
According to the law, even with an infinite number of processors, the speedup is constrained by the unparallelizable portion. In computer architecture, Amdahl's law (or Amdahl's argument [1]) is a formula that shows how much faster a task can be completed when you add more resources to the system. The law can be stated as:
Work law. The cost is always at least the work: pT p ≥ T 1. This follows from the fact that p processors can perform at most p operations in parallel. [6] [9] Span law. A finite number p of processors cannot outperform an infinite number, so that T p ≥ T ∞. [9] Using these definitions and laws, the following measures of performance can be ...
The Karp–Flatt metric is a measure of parallelization of code in parallel processor systems. This metric exists in addition to Amdahl's law and Gustafson's law as an indication of the extent to which a particular computer code is parallelized. It was proposed by Alan H. Karp and Horace P. Flatt in 1990.
For simple loops, where each iteration is independent of the others, loop-level parallelism can be embarrassingly parallel, as parallelizing only requires assigning a process to handle each iteration. However, many algorithms are designed to run sequentially, and fail when parallel processes race due to dependence within the code. Sequential ...
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An initial version of this model was introduced, under the MapReduce name, in a 2010 paper by Howard Karloff, Siddharth Suri, and Sergei Vassilvitskii. [2] As they and others showed, it is possible to simulate algorithms for other models of parallel computation, including the bulk synchronous parallel model and the parallel RAM, in the massively parallel communication model.