August 2018
Intermediate to advanced
227 pages
4h 16m
Chinese
Content preview from 高效R语言编程
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152
｜
第
7
章
library("parallel")
no_of_cores = detectCores()
detectCores() 的返回值取决于操作系统与芯片制造商，详见
help("detectCores")。对于多数标准机器，detectCores() 返回同时运行的
线程数。
Apply
函数的并行版本
并行应用程序中常见的函数是 lapply()、sapply() 与 apply() 的并行化替代
函数。下面代码显示并行化的版本以及它们的参数：
parLapply(cl, x, FUN, ...)
parApply(cl = NULL, X, MARGIN, FUN, ...)
parSapply(cl = NULL, X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE)
关键一点是 parLapply() 与 apply() 之间的参数差异很少，所以如果你已熟
悉
apply
家族的函数，进行并行化计算的门槛是很低的。这些函数中的每一个
都有一个 makeCluster() 函数调用时生成的参数 cl。该函数除别的功能外，
指定了要使用的
CPU
数目。
例子：蛇梯棋游戏
并行计算是
Monte Carlo
模拟的理想选择。每个核独立模拟了该模型的实
现。最后，我们收集结果。在
efficient
包，有一个模拟蛇梯棋游戏的函数；
snakes_ladders()。
注
3
下面代码演示了如何使用 sapply() 模拟
N
游戏：
N = 10^4
sapply(1:N, ...
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