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统计函数的并行计算或分布式计算

statistics and machine learning toolbox™ 允许您使用并行计算来加快某些统计计算的速度。在并行计算中，一个 matlab^® 客户端会话将代码段分配给多个工作进程进行独立处理，然后将各个结果合并起来完成计算。使用并行计算，可以加快重抽样方法（例如自助法和 jackknife 法、决策树提升法和装袋法、交叉验证法、聚类算法等）的速度。有关 statistics and machine learning toolbox 中支持并行计算的完整函数列表，请参阅函数列表（自动并行支持）。

有些函数接受 gpuarray (parallel computing toolbox) 输入参数，因此您可以通过在图形处理单元 (gpu) 上运行来加快代码执行速度。有关接受 gpu 数组的 statistics and machine learning toolbox 函数的完整列表，请参阅函数列表（gpu 数组）。

您必须拥有 parallel computing toolbox™ 许可证才能使用并行计算功能和 gpu 数组。

主题

quick start parallel computing for statistics and machine learning toolbox
get started with parallel statistical computing.
concepts of parallel computing in statistics and machine learning toolbox
overview of the ideas in parallel statistical computations.
when to run statistical functions in parallel
deciding when to call functions in parallel.
working with parfor
parallel computing using parfor with statistics functions.
implement jackknife using parallel computing
speed up the jackknife using parallel computing.
implement cross-validation using parallel computing
speed up cross-validation using parallel computing.
implement bootstrap using parallel computing
speed up the bootstrap using parallel computing.
reproducibility in parallel statistical computations
how to obtain identical results from repeated parallel computations.
analyze and model data on gpu
accelerate your code by using gpu array input arguments.
accelerate linear model fitting on gpu
this example shows how you can accelerate regression model fitting by running functions on a graphical processing unit (gpu).

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