August 2018
Intermediate to advanced
227 pages
4h 16m
Chinese
Content preview from 高效R语言编程
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154
｜
第
7
章
在
Linux
与
OSX
下的并行代码
如果你正在使用
Linux
或者
OS X
操作系统，以并行方式运行代码的另外一种
方式是使用 mclapply() 与 mcmapply() 函数：
#
这可以运行于
Windows
，但仅用了一个核
mclapply(1:N, snakes_ladders)
这些函数使用了分支（
fork
），就是说创建了正在运行线程的新复制品。然而，
Windows
系统不支持
Linux
下的这种底层功能。mclapply() 的主要优点是你
不必启动和停止集群对象。最大缺点是在
Windows
电脑下，你被限制到单核上。
Rcpp
有时
R
就是慢，你尝试了各种方法而你的代码仍旧像老牛拉破车一样慢。
此时你应该考虑使用其他较快的语言编写代码的核心部分。通过
Rcpp
、
rJava
、
rPython
以及最近的
V8
之类的包，
R
提供了与其他语言的接口。这
些包分别提供了与
C++
、
Java
、
Python
、
JavaScript
语言的接口。
Rcpp
是其
中最流行的（见图
7-5
）。
C++
是一个现代的、快速的并具有较强支持度的语言，包含大量的计算各种
任务的库。
Rcpp
可轻松将
C++
融合到你的
R
工作流中。
尽管
C/Fortran
程序支持 .Call() 函数的调用形式，但不推荐这么用，因为使
用 .Call() 是一个痛苦的过程。
Rcpp
提供了一个友好的
API
，它允许你绕过
R
棘手的
C API
，编写高性能的代码。
C++
中典型瓶颈是地址循环与递归函数。
C++
是一门强大编程语言，有大量的书籍描述其内容。因此本节中重点关注 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.