我读到,R只使用一个单一的CPU。 如何让R利用所有可用核心来管理统计算法?
是的,关于开端人,见。 高额电子计算 森林问题合作伙伴关系。 该清单详细列出了可用于支持单一机器平行计算的包裹。
从2.14.0R版开始,通过parallel Pack为平行计算提供了内在支持,其中包括现有snow和多核心包。 <<>parallel>/strong> 一揽子计划有一份你应读的传真。 你们可以使用:
vignette(package="parallel", topic = "parallel")
还有其他利用你多种核心的方法,例如通过使用多面的生物浓缩系数进行线性黄色计算。
不管怎样才能加快“统计计算”,你想要做的将取决于这些“统计计算”是什么。 穿过多个深层或工人需要间接费用才能建立、管理和收集结果。 一些行动认为,使用多个核心/校对金(大体,小一些)是一种好处,另一些则由于这种额外间接费用而放缓。
简言之,预计不会收到n。 页: 1 核心而不是仅仅1。
If you happen to do few* iterations of the same thing (or same code for few* different parameters), the easiest way to go is to run several copies of R -- OS will allocate the work on different cores.
In the opposite case, go and learn how to use real parallel extensions.
为本回答的目的,很少意味着核心数目减少或相等。
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