我想要利用matlab MEX file来加速 我遇到了一个问题是 MEX的速度应该会比matlab快上几倍 而且Eigen应该会比matlab快上10~30倍 但是我的matlab跑一次是0.1707 sec 我的mex跑一次是 0.667856 sec 为什麽我的mex可以那麽慢，有大大可以指导我一下吗？ 附上我完整的code #include "mex.h" #include "Eigen/Dense" #include "Eigen/Core" #include <iostream> using namespace Eigen; using namespace std; typedef Map<MatrixXd> MexMat; void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) { ptrdiff_t n_de = mxGetM(prhs[0]), n_basis = mxGetN(prhs[0]); ptrdiff_t n_nu = mxGetM(prhs[1]); ptrdiff_t n_w = mxGetN(prhs[2]) , n_p = mxGetN(prhs[3]); ptrdiff_t n_min = min(n_de, n_nu); MexMat diff_SQ_de ( mxGetPr(prhs[0]), n_de, n_basis); MexMat diff_SQ_nu ( mxGetPr(prhs[1]), n_nu, n_basis); MexMat width_candidates ( mxGetPr(prhs[2]), 1, n_w); MexMat panelty_candidates ( mxGetPr(prhs[3]), 1, n_p); MatrixXd ker_de_tr(n_de, n_basis), ker_nu_tr(n_nu, n_basis); MatrixXd H_hat(n_basis, n_basis), h_hat(n_basis, n_basis); MatrixXd ker_de_tr2(n_min, n_basis), ker_nu_tr2(n_min, n_basis); MatrixXd B(n_basis, n_basis), Beta(n_basis, 1), B_de(n_basis, n_min); MatrixXd tmp(1, n_min), B0(n_basis, n_min), B1(n_basis, n_min); MatrixXd A(n_basis, n_min), r_de(n_min,1), r_nu(n_min,1); MatrixXd CV_score (n_w, n_p), width(1,1), panelty(1,1); for (int width_run = 0; width_run < n_w; width_run++){ width(0,0) = width_candidates(0, width_run); ker_de_tr = (diff_SQ_de.array() / (-2) / pow(width(0,0),2)).array().exp(); ker_nu_tr = (diff_SQ_nu.array() / (-2) / pow(width(0,0),2)).array().exp(); H_hat = ker_de_tr.transpose() * ker_de_tr / n_de; h_hat = ((ker_nu_tr / n_nu).colwise().sum()).transpose(); ker_de_tr2 = (ker_de_tr.block(0,0,n_min,n_basis)).transpose(); ker_nu_tr2 = (ker_nu_tr.block(0,0,n_min,n_basis)).transpose(); for (int panelty_run = 0; panelty_run < n_p; panelty_run++){ panelty(0,0) = panelty_candidates(0, panelty_run); H_hat.diagonal() = (H_hat.diagonal()).array() + panelty(0,0)*(n_de-1)/n_de; B = H_hat.inverse(); Beta = B * h_hat; B_de = B * ker_de_tr2; tmp = ((ker_de_tr2.cwiseProduct(B_de)).colwise().sum()).array() * (-1) + n_de; B0 = (Beta.replicate(1, n_min) + (((Beta.transpose() * ker_de_tr2) .cwiseQuotient(tmp)).replicate(n_basis, 1).cwiseProduct(B_de))) * n_nu; B1 = B*ker_nu_tr2 + B_de.cwiseProduct(((((ker_nu_tr2.cwiseProduct( B_de)).colwise().sum()).cwiseQuotient(tmp)).replicate(n_basis,1))); A = ((B0-B1) * (n_de-1)/(n_de*(n_nu-1))).cwiseMax(MatrixXd::Zero(n_basis ,n_min)); r_de = (ker_de_tr2.cwiseProduct(A)).colwise().sum(); r_nu = (ker_nu_tr2.cwiseProduct(A)).colwise().sum(); CV_score(width_run, panelty_run) = (r_de.array().pow(2)/n_de).sum()/2 - (r_nu / n_nu).sum(); } } int loc_w, loc_p; MatrixXd min_cv_score; CV_score.minCoeff(&loc_p, &loc_w); MatrixXd O1(1,1), O2(1,1); O1(0,0) = width_candidates(0,loc_w); O2(0,0) = panelty_candidates(0,loc_p); plhs[0] = mxCreateDoubleMatrix(1, 1, mxREAL); plhs[1] = mxCreateDoubleMatrix(1, 1, mxREAL); MexMat Output1 ( mxGetPr(plhs[0]), 1, 1 ); MexMat Output2 ( mxGetPr(plhs[1]), 1, 1 ); Output1 = O1; Output2 = O2; } 我在猜想是不是每次取代这麽多东西造成记忆体不断再复制，而拖慢 希望有人可以帮我找到我哪里写得不好导致matlab比较快。 最後补上我的系统资讯 file (include test.m): http://myweb.ncku.edu.tw/~r26014014/uLSIF.rar Platform OS: windows 7 SP1 64bit Matlab version: 2013b compiler: VS 2012 compile command: mex -v -largeArrayDims -IC:\Eigen mex_file.cpp COMPFLAGS="/Ox $COMPFLAGS" 另外，我有google到这篇：http://tinyurl.com/mzhajo6 最後，如果解决问题需要我全部的程式码，可以寄站内信。 --

※ 发信站: 批踢踢实业坊(ptt.cc) ◆ From: 140.116.152.221 我google之後，加上/Ox之後，还是要0.6678 sec 我的case更加复杂，K1还会用在其他地方.... 此外这里有L2 panelty term 大大可以给我一点实际例子吗？ 因为我run出来不是LS... 我自己去run了一个test A is 3 by 3 matrix. b is 3 by 1 matrix A.colPivHouseholderQr.solve(b) is equal to A.lu().solve(b) not the inv(A'A)*b test code: #include <iostream> #include <Eigen/Dense> #include <Eigen/Core> using namespace Eigen; using namespace std; typedef Map<MatrixXd> MexMat; int main() { double mat[9] = {-1.0,-2.0,-3.0,4.0, 5.5, 6.5, -7.7, -8.0, 11.0}; double mat2[3] = {1.0,1.0,3.0}; MexMat EiMat (mat, 3, 3); MexMat EiMat2 (mat2, 3, 1); cout << EiMat.colPivHouseholderQr().solve(EiMat2) << endl; cout << EiMat.lu().solve(EiMat2) << endl; } Result: 1.11111 0.765432 0.123457 1.11111 0.765432 0.123457 ※ 编辑: celestialgod 来自: 36.238.92.7 (12/24 01:59)