Rcppparallel bootstrap

Rcppparallel bootstrap

我假设,或者更确切地说,我希望,我有一个单一的可解决的问题,或者可能是许多较小的问题,应该放弃。无论哪种方式,我都是 Rcpp 的新手,对并行计算非常不了解,无法在线找到解决方案。

问题通常是,R 或 R 中的 'fatal error' 陷入循环,大约 5 分钟进行 10 次迭代,而非并行版本将同时进行 5K 次迭代,大致正在发言。

由于该算法适用于一个更大的项目,我调用了其他几个函数,这些都在 Rcpp 中,我只用 'arma' 个对象重写了它们,因为这似乎可以帮助其他人,在这里。我还 运行 使用我在 Rcpp 中编写的 'heat map' 优化器的优化部分,再次完全在 'arma' 中没有改进 - 我还应该指出这作为 'arma::vec' 返回。

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

struct Boot_Worker : public Worker {

  //Generate Inputs
  // Source vector to keep track of the number of bootstraps
  const arma::vec Boot_reps;

  // Initial non-linear theta parameter values
  const arma::vec init_val;

  // Decimal date vector
  const arma::colvec T_series;

  // Generate the price series observational vector
  const arma::colvec Y_est;
  const arma::colvec Y_res;

  // Generate the optimization constants
  const arma::mat U;
  const arma::colvec C;

  const int N;

  // Generate Output Matrix
  arma::mat Boots_out;

  // Initialize with the proper input and output
  Boot_Worker( const arma::vec Boot_reps, const arma::vec init_val, const arma::colvec T_series, const arma::colvec Y_est, const arma::colvec Y_res, const arma::mat U, const arma::colvec C, const int N, arma::mat Boots_out)
    : Boot_reps(Boot_reps), init_val(init_val), T_series(T_series), Y_est(Y_est), Y_res(Y_res), U(U), C(C), N(N), Boots_out(Boots_out) {}

  void operator()(std::size_t begin, std::size_t end){
    //load necessary stuffs from around

    Rcpp::Environment stats("package:stats");
    Rcpp::Function constrOptim = stats["constrOptim"];
    Rcpp::Function SDK_pred_mad( "SDK_pred_mad");

    arma::mat fake_data(N,2);
    arma::colvec index(N);

    for(unsigned int i = begin; i < end; i ++){

      // Need a nested loop to create and fill the fake data matrix

      arma::vec pool = arma::regspace(0, N-1) ;
      std::random_shuffle(pool.begin(), pool.end());
      for(int k = 0; k <= N-1; k++){
        fake_data(k, 0) = Y_est[k] + Y_res[ pool[k] ];
        fake_data(k, 1) = T_series[k];
      }

      // Call the optimization
      Rcpp::List opt_results = constrOptim(Rcpp::_["theta"]    = init_val,
                                           Rcpp::_["f"]     = SDK_pred_mad,
                                           Rcpp::_["data_in"] = fake_data,
                                           Rcpp::_["grad"] = "NULL",
                                           Rcpp::_["method"] = "Nelder-Mead",
                                           Rcpp::_["ui"] = U,
                                           Rcpp::_["ci"] = C );

      /// fill the output matrix ///

      // need to create an place holder arma vector for the parameter output
      arma::vec opt_param = Rcpp::as<arma::vec>(opt_results[0]);
      Boots_out(i, 0) = opt_param[0];
      Boots_out(i, 1) = opt_param[1];
      Boots_out(i, 2) = opt_param[2];
      // for the cost function value at optimization
      arma::vec opt_value = Rcpp::as<arma::vec>(opt_results[1]);
      Boots_out(i, 3) = opt_value[0];
      // for the number of function calls (?)
      arma::vec counts = Rcpp::as<arma::vec>(opt_results[2]);
      Boots_out(i, 4) = counts[0];
      // for thhe convergence code
      arma::vec convergence = Rcpp::as<arma::vec>(opt_results[3]);
      Boots_out(i, 5) = convergence[0];

    }

  }

};

// [[Rcpp::export]]
arma::mat SDK_boots_test(arma::vec init_val, arma::mat data_in, int boots_n){

  //First establish theta_sp, estimate and residuals
  const int N = arma::size(data_in)[0];

  // Create the constraints for the constrained optimization
  // Make a boundry boundry condition matrix of the form Ui*theta - ci >= 0
  arma::mat U(6, 3);

  U(0, 0) = 1;
  U(1, 0) = -1;
  U(2, 0) = 0;
  U(3, 0) = 0;
  U(4, 0) = 0;
  U(5, 0) = 0;

  U(0, 1) = 0;
  U(1, 1) = 0;
  U(2, 1) = 1;
  U(3, 1) = -1;
  U(4, 1) = 0;
  U(5, 1) = 0;

  U(0, 2) = 0;
  U(1, 2) = 0;
  U(2, 2) = 0;
  U(3, 2) = 0;
  U(4, 2) = 1;
  U(5, 2) = -1;

  arma::colvec C(6);
  C[0] = 0;
  C[1] =  -data_in(N-1, 9)-0.5;
  C[2] = 0;
  C[3] = -3;
  C[4] = 0;
  C[5] = -50;

  Rcpp::Function SDK_est( "SDK_est");
  Rcpp::Function SDK_res( "SDK_res");

  arma::vec Y_est = as<arma::vec>(SDK_est(init_val, data_in));
  arma::vec Y_res = as<arma::vec>(SDK_res(init_val, data_in));

  // Generate feed items for the Bootstrap Worker
  arma::vec T_series = data_in( span(0, N-1), 9);

  arma::vec Boots_reps(boots_n+1);

  // Allocate the output matrix
  arma::mat Boots_out(boots_n, 6);

  // Pass input and output the Bootstrap Worker
  Boot_Worker Boot_Worker(Boots_reps, init_val, T_series, Y_est, Y_res, U, C, N, Boots_out);

  // Now finnaly call the parallel for loop
  parallelFor(0, Boots_reps.size(), Boot_Worker);

  return Boots_out;
}

所以我在我的 'heat algorithm' 中回复了解决优化的问题,这完全在 Rcpp-armadillo 中,这大大简化了代码,因为约束被写入优化器。此外,我删除了运行domization,所以它只需要解决相同的优化;只是为了看看这是不是唯一的问题。毫无疑问,我仍然拥有相同的 'fatal error'.

这里的代码是:

// [[Rcpp::depends("RcppArmadillo")]]
// [[Rcpp::depends("RcppParallel")]]
#include <RcppArmadillo.h>
#include <RcppParallel.h>
#include <random>
using namespace Rcpp;
using namespace std;
using namespace arma;
using namespace RcppParallel;

struct Boot_Worker : public Worker {

  //Generate Inputs
  // Source vector to keep track of the number of bootstraps
  const arma::vec Boot_reps;

  // Initial non-linear theta parameter values
  const arma::vec init_val;

  // Decimal date vector
  const arma::colvec T_series;

  // Generate the price series observational vector
  const arma::colvec Y_est;
  const arma::colvec Y_res;

  const int N;

  // Generate Output Matrix
  arma::mat Boots_out;

  // Initialize with the proper input and output
  Boot_Worker( const arma::vec Boot_reps, const arma::vec init_val, const arma::colvec T_series, const arma::colvec Y_est, const arma::colvec Y_res, const int N, arma::mat Boots_out)
    : Boot_reps(Boot_reps), init_val(init_val), T_series(T_series), Y_est(Y_est), Y_res(Y_res), N(N), Boots_out(Boots_out) {}

  void operator()(std::size_t begin, std::size_t end){
    //load necessary stuffs from around

    Rcpp::Function SDK_heat( "SDK_heat");

    arma::mat fake_data(N,2);
    arma::colvec index(N);

    for(unsigned int i = begin; i < end; i ++){

      // Need a nested loop to create and fill the fake data matrix

      //arma::vec pool = arma::shuffle( arma::regspace(0, N-1) );

      for(int k = 0; k <= N-1; k++){
        fake_data(k, 0) = Y_est[k] + Y_res[ k ];
        //fake_data(k, 0) = Y_est[k] + Y_res[ pool[k] ];
        fake_data(k, 1) = T_series[k];
      }

      // Call the optimization

      arma::vec opt_results = Rcpp::as<arma::vec>(  SDK_heat(Rcpp::_["data_in"]    = fake_data, Rcpp::_["tol"]     = 0.1) );


      /// fill the output matrix ///

      // need to create an place holder arma vector for the parameter output
      Boots_out(i, 0) = opt_results[0];
      Boots_out(i, 1) = opt_results[1];
      Boots_out(i, 2) = opt_results[2];
      // for the cost function value at optimization
      Boots_out(i, 3) = opt_results[3];

    }

  }

};

// [[Rcpp::export]]
arma::mat SDK_boots_test(arma::vec init_val, arma::mat data_in, int boots_n){

  //First establish theta_sp, estimate and residuals
  const int N = arma::size(data_in)[0];


  Rcpp::Function SDK_est( "SDK_est");
  Rcpp::Function SDK_res( "SDK_res");

  const arma::vec Y_est = as<arma::vec>(SDK_est(init_val, data_in));
  const arma::vec Y_res = as<arma::vec>(SDK_res(init_val, data_in));

  // Generate feed items for the Bootstrap Worker
  const arma::vec T_series = data_in( span(0, N-1), 9);

  arma::vec Boots_reps(boots_n+1);

  // Allocate the output matrix
  arma::mat Boots_out(boots_n, 4);

  // Pass input and output the Bootstrap Worker
  Boot_Worker Boot_Worker(Boots_reps, init_val, T_series, Y_est, Y_res, N, Boots_out);

  // Now finnaly call the parallel for loop
  parallelFor(0, Boots_reps.size(), Boot_Worker);

  return Boots_out;
}

查看您的代码,我看到以下内容:

struct Boot_Worker : public Worker {
  [...]      
  void operator()(std::size_t begin, std::size_t end){
    //load necessary stuffs from around
    
    Rcpp::Environment stats("package:stats");
    Rcpp::Function constrOptim = stats["constrOptim"];
    Rcpp::Function SDK_pred_mad( "SDK_pred_mad");
    
    [...]
      
      // Call the optimization
      Rcpp::List opt_results = constrOptim(Rcpp::_["theta"]    = init_val,
                                           Rcpp::_["f"]     = SDK_pred_mad,
                                           Rcpp::_["data_in"] = fake_data,
                                           Rcpp::_["grad"] = "NULL",
                                           Rcpp::_["method"] = "Nelder-Mead",
                                           Rcpp::_["ui"] = U,
                                           Rcpp::_["ci"] = C );

您正在从多线程 C++ 上下文中调用 R 函数。那是你 should not do 的东西。 R 是单线程的,因此这将导致未定义的行为或崩溃:

API Restrictions

The code that you write within parallel workers should not call the R or Rcpp API in any fashion. This is because R is single-threaded and concurrent interaction with it’s data structures can cause crashes and other undefined behavior. Here is the official guidance from Writing R Extensions:

Calling any of the R API from threaded code is ‘for experts only’: they will need to read the source code to determine if it is thread-safe. In particular, code which makes use of the stack-checking mechanism must not be called from threaded code.

此外,即使在单线程上下文中从 C++ 回调 R 也不是您可以为性能做的最好的事情。使用提供直接 C(++) 接口的优化库应该更有效。一种可能是 nlopt 的开发版本,c.f。 this issue for a discussion and references to examples. In addition, std::random_shuffle is not only deprecated in C++14 and removed from C++17, but it is also not thread-safe.

在你的第二个例子中,你说函数 SDK_heat 实际上是用 C++ 实现的。在那种情况下你可以直接调用它:

  • 去掉导入相应的R函数,即Rcpp::Function SDK_heat( "SDK_heat");
  • 确保编译器知道 C++ 函数的声明并且链接器具有实际函数:
    • 快速而肮脏:将函数定义复制到 cpp 文件中,然后再定义 BootWorker
    • 要获得更清晰的方法,请参阅 Rcpp attributes vignette
    • 中的“1.10 共享代码”部分
  • 像调用任何其他 C++ 函数一样调用该函数,即使用类型与函数声明兼容的位置参数。

所有这些都假定您正在使用 sourceCpp,正如您对 [[Rcpp::depends(...)]] 的使用所表明的那样。您正在达到一种复杂性,可以保证从中构建一个包。