RCPP编程效率 [英] Rcpp programming efficiency

问题描述

我是Rcpp的初学者.目前，我编写了一个Rcpp代码，该代码应用于两个3维数组: Array1 和 Array2 .假设 Array1 的尺寸为(1000，100，40)，而 Array2 的尺寸为(1000，96，40).

I am a beginner with Rcpp. Currently I wrote a Rcpp code, which was applied on two 3 dimensional arrays: Array1 and Array2. Suppose Array1 has dimension (1000, 100, 40) and Array2 has dimension (1000, 96, 40).

我想使用以下命令执行 wilcox.test :

I would like to perform wilcox.test using:

wilcox.test(Array1[i, j,], Array2[i,,])

在 R 中，我编写了嵌套的 for 循环，该循环在大约半小时内完成了计算.

In R, I wrote nested for loops that completed the calculation in about a half hour.

然后，我将其写入Rcpp.Rcpp中的计算花了一个小时才能达到相同的结果.我认为它应该是更快的，因为它是用C ++语言编写的.我想我的编码风格是效率低下的原因.

Then, I wrote it into Rcpp. The calculation within Rcpp took an hour to achieve the same results. I thought it should be faster since it is written in C++ language. I guess that my style of coding is the cause of the low efficient.

以下是我的Rcpp代码，您介意帮助我确定我应该进行哪些改进吗?我很感激！

The following is my Rcpp code, would you mind helping me find out what improvement should I make please? I appreciate it!

// [[Rcpp::depends(RcppArmadillo)]]
#include <RcppArmadillo.h>
using namespace Rcpp;

// [[Rcpp::export]]
NumericVector Cal(NumericVector Array1,NumericVector Array2,Function wilc) {

  NumericVector vecArray1(Array1);
  IntegerVector arrayDims1 = vecArray1.attr("dim");

  NumericVector vecArray2(Array2);
  IntegerVector arrayDims2 = vecArray2.attr("dim");

  arma::cube cubeArray1(vecArray1.begin(), arrayDims1[0], arrayDims1[1],      arrayDims1[2], false);

  arma::cube cubeArray2(vecArray2.begin(), arrayDims2[0], arrayDims2[1], arrayDims2[2], false);

  arma::mat STORE=arma::mat(arrayDims1[0], arrayDims1[1]); 

  for(int i=0;i<arrayDims1[1];i++)
  { 

    for(int j=0;j<arrayDims1[0];j++){
      arma::vec       v_cl=cubeArray1.subcube(arma::span(j),arma::span(i),arma::span::all);

      //arma::mat      tem=cubeArray2.subcube(arma::span(j),arma::span::all,arma::span::all);

      //arma::vec v_ct=arma::vectorise(tem);

      arma::vec   v_ct=arma::vectorise(cubeArray2.subcube(arma::span(j),arma::span::all,arma::span::all));

      Rcpp::List resu=wilc(v_cl,v_ct);
      STORE(j,i)=resu[2];

    }

  }


  return(Rcpp::wrap(STORE));

}

函数 wilc 将是 R 中的 wilcox.test .

以下是我的 R 代码的一部分，用于实现上述想法，其中 CELLS 和 CTRLS 是中的两个3D数组R .

The following is part of my R code for implementing the above idea, where CELLS and CTRLS are two 3D array in R.

for(i in 1:ncol(CELLS)) {
  if(T){ print(i) }
  for (j in 1:dim(CELLS)[1]) {
    wtest = wilcox.test(CELLS[j,i,], CTRLS[j,,])
    TSTAT_clcl[j,i] = wtest$p.value
  }
}

推荐答案

然后，我将其写入Rcpp.Rcpp中的计算花了一个小时才能达到相同的结果.我认为它应该是更快的，因为它是用C ++语言编写的.

Then, I wrote it into Rcpp. The calculation within Rcpp took an hour to achieve the same results. I thought it should be faster since it is written in C++ language.

必需的免责声明:

在 C ++ 中嵌入 R 代码，并期望提高速度是傻瓜的游戏.您将需要用 C ++ 完全重写 wilcox.test ，而不是调用 R .否则，您将失去获得的任何加速优势.

Embedding R code in C++ and expecting a speed up is a fool's game. You will need to rewrite wilcox.test full in C++ instead of making a call to R. Otherwise, you lose whatever speed up advantage you get.

我特别写了一个 post 说明了有关在 R 中使用 diff 函数的难题.在帖子中，我详细比较了例程中使用 R 函数的 pure C ++ 实现和 C ++ 实现.，以及纯 R 实现.窃取 microbenchmark 可以说明上述问题.

In particular, I wrote up a post illustrating this conundrum regarding the using the diff function in R. Within the post, I detailed comparing a pure C++ implementation, an C++ implementation using an R function within the routine, and a pure R implementation. Stealing the microbenchmark illustrates the above issue.

expr        min     lq      mean        median  uq      max         neval
arma_fun    26.117  27.318  37.54248    28.218  29.869  751.087     100
r_fun       127.883 134.187 212.81091   138.390 151.148 1012.856    100
rcpp_fun    250.663 265.972 356.10870   274.228 293.590 1430.426    100

因此，纯 C ++ 实现的速度最快.

Thus, a pure C++ implementation had the largest speed up.

因此，需要解决的是翻译

Hence, the take away is the need to translate the wilcox.test R routine code to a pure C++ implementation to drop the run time. Otherwise, it is meaningless to write the code in C++ because the C++ component must stop and await results from R before continuing. This traditionally has a lot of overhead to ensure the data is well protected.

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