Rcpp 并行化返回 XPtr 的函数? [英] Rcpp parallelize functions that return XPtr?

问题描述

以XPtr函数为例:

test.cpp

#include <Rcpp.h>

// [[Rcpp::export]]
SEXP funx()
{
    /* creating a pointer to a vector<int> */
    std::vector<int>* v = new std::vector<int> ;
    v->push_back( 1 ) ;
    v->push_back( 2 ) ;

    /* wrap the pointer as an external pointer */
    /* this automatically protected the external pointer from R garbage 
     collection until p goes out of scope. */
    Rcpp::XPtr< std::vector<int> > p(v, true) ;

    /* return it back to R, since p goes out of scope after the return 
     the external pointer is no more protected by p, but it gets 
     protected by being on the R side */
    return( p ) ;
}

R

library(Rcpp)
sourceCpp("test.cpp")

xp <- funx()
xp
<pointer: 0x9618cc0>

但是如果我尝试并行化，我会得到空指针

But if I try to parallelize this I get null pointers

library(parallel)
out <- mclapply(1:2, function(x) funx())
out
[[1]]
<pointer: (nil)>

[[2]]
<pointer: (nil)>

有没有可能实现这种功能?

Is it possible to achieve this kind of functionality?

编辑

值得注意的是，尽管存在重复问题，但似乎没有真正解决此问题的方法.根据我现在的理解，XPtr 不能是多线程的.所以基本上这不能在 R 中完成.

It is worth noting that despite a duplicate question there appears to be no true solution to this problem. From what I understand now, an XPtr is not able to be multi-threaded. So essentially this cannot be done in R.

例如，当我将函数放入包 test 并尝试使用 snow 时，它仍然无法返回指针.

For example, when I put the function inside package test and try to use snow it still fails to return the pointers.

library(test)
library(snow)

fun <- function(){
  library(test)
  test:::funx()
}

cl <- makeCluster(2, type = "SOCK") 
clusterExport(cl, 'fun') 
clusterCall(cl, fun)
[[1]]
<pointer: (nil)>

[[2]]
<pointer: (nil)>

推荐答案

关于

有没有可能实现这种功能?

Is it possible to achieve this kind of functionality?

我会说答案是非常坚决的不"，因为 搏击俱乐部的第一条规则 在这里适用:您只是不能仅仅通过希望它能够工作来并行化底层 R 实例.像 RcppParallel 这样的包对于将非 R 数据结构用于多线程非常小心工作.

I would say the answer is a pretty firm 'nope' as the First Rule of Fight Club applies here: you simply cannot parallelise the underlying R instance merely by hoping it would work. Packages like RcppParallel are very careful about using non-R data structures for multithreaded work.

我可能过于悲观，但我会将集合级别"更深一层，并且只将其聚合结果返回给 R.

I may be too pessimistic but I would place the 'collection level' one level deeper, and only return its aggregated result to R.

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