Question

在我的开发区域（wamp），一切正常。今天我将整个项目上传到我的生产网站空间。

我似乎在生产区域，有些路径丢失了。首先，我在开始布局（ZEND_Layout）中看到我的图片找不到。所以我需要改变图片的路径：

$ nvcc -std=c++11 cusparse_test3.cu -o cusparse_test3 -lcusparse -lcusolver
cusparse_test3.cu(208): error: identifier "cusolverSpScsrlsvlu" is undefined

在我的登录表单之后，我改变了我的布局，在那里我离开了以前的路径并且它有效：

#include <iostream>
#include <cuda_runtime.h>
#include <cuda.h>
#include <cusolverSp.h>
#include <cusparse.h>
#include <vector>
#include <cassert>


// create poisson matrix with Dirichlet bc. of a rectangular grid with
// dimension NxN
void assemble_poisson_matrix_coo(std::vector<float>& vals, std::vector<int>& row, std::vector<int>& col,
                     std::vector<float>& rhs, int Nrows, int Ncols) {

        //nnz: 5 entries per row (node) for nodes in the interior
    // 1 entry per row (node) for nodes on the boundary, since we set them explicitly to 1.
    int nnz = 5*Nrows*Ncols - (2*(Ncols-1) + 2*(Nrows-1))*4;
    vals.resize(nnz);
    row.resize(nnz);
    col.resize(nnz);
    rhs.resize(Nrows*Ncols);

    int counter = 0;
    for(int i = 0; i < Nrows; ++i) {
        for (int j = 0; j < Ncols; ++j) {
            int idx = j + Ncols*i;
            if (i == 0 || j == 0 || j == Ncols-1 || i == Nrows-1) {
                vals[counter] = 1.;
                row[counter] = idx;
                col[counter] = idx;
                counter++;
                rhs[idx] = 1.;
//                if (i == 0) {
//                    rhs[idx] = 3.;
//                }
            } else { // -laplace stencil
                // above
                vals[counter] = -1.;
                row[counter] = idx;
                col[counter] = idx-Ncols;
                counter++;
                // left
                vals[counter] = -1.;
                row[counter] = idx;
                col[counter] = idx-1;
                counter++;
                // center
                vals[counter] = 4.;
                row[counter] = idx;
                col[counter] = idx;
                counter++;
                // right
                vals[counter] = -1.;
                row[counter] = idx;
                col[counter] = idx+1;
                counter++;
                // below
                vals[counter] = -1.;
                row[counter] = idx;
                col[counter] = idx+Ncols;
                counter++;

                rhs[idx] = 0;
            }
        }
    }
    assert(counter == nnz);
}



int main() {
    // --- create library handles:
    cusolverSpHandle_t cusolver_handle;
    cusolverStatus_t cusolver_status;
    cusolver_status = cusolverSpCreate(&cusolver_handle);
    std::cout << "status create cusolver handle: " << cusolver_status << std::endl;

    cusparseHandle_t cusparse_handle;
    cusparseStatus_t cusparse_status;
    cusparse_status = cusparseCreate(&cusparse_handle);
    std::cout << "status create cusparse handle: " << cusparse_status << std::endl;

    // --- prepare matrix:
    int Nrows = 4;
    int Ncols = 4;
    std::vector<float> csrVal;
    std::vector<int> cooRow;
    std::vector<int> csrColInd;
    std::vector<float> b;

    assemble_poisson_matrix_coo(csrVal, cooRow, csrColInd, b, Nrows, Ncols);

    int nnz = csrVal.size();
    int m = Nrows * Ncols;
    std::vector<int> csrRowPtr(m+1);

    // --- prepare solving and copy to GPU:
    std::vector<float> x(m);
    float tol = 1e-5;
    int reorder = 0;
    int singularity = 0;

    float *db, *dcsrVal, *dx;
    int *dcsrColInd, *dcsrRowPtr, *dcooRow;
    cudaMalloc((void**)&db, m*sizeof(float));
    cudaMalloc((void**)&dx, m*sizeof(float));
    cudaMalloc((void**)&dcsrVal, nnz*sizeof(float));
    cudaMalloc((void**)&dcsrColInd, nnz*sizeof(int));
    cudaMalloc((void**)&dcsrRowPtr, (m+1)*sizeof(int));
    cudaMalloc((void**)&dcooRow, nnz*sizeof(int));

    cudaMemcpy(db, b.data(), b.size()*sizeof(float), cudaMemcpyHostToDevice);
    cudaMemcpy(dcsrVal, csrVal.data(), csrVal.size()*sizeof(float), cudaMemcpyHostToDevice);
    cudaMemcpy(dcsrColInd, csrColInd.data(), csrColInd.size()*sizeof(int), cudaMemcpyHostToDevice);
    cudaMemcpy(dcooRow, cooRow.data(), cooRow.size()*sizeof(int), cudaMemcpyHostToDevice);

    cusparse_status = cusparseXcoo2csr(cusparse_handle, dcooRow, nnz, m,
                                       dcsrRowPtr, CUSPARSE_INDEX_BASE_ZERO);
    std::cout << "status cusparse coo2csr conversion: " << cusparse_status << std::endl;

    cudaDeviceSynchronize(); // matrix format conversion has to be finished!

    // --- everything ready for computation:

    cusparseMatDescr_t descrA;

    cusparse_status = cusparseCreateMatDescr(&descrA);
    std::cout << "status cusparse createMatDescr: " << cusparse_status << std::endl;

    // optional: print dense matrix that has been allocated on GPU

    std::vector<float> A(m*m, 0);
    float *dA;
    cudaMalloc((void**)&dA, A.size()*sizeof(float));

    cusparseScsr2dense(cusparse_handle, m, m, descrA, dcsrVal,
                       dcsrRowPtr, dcsrColInd, dA, m);

    cudaMemcpy(A.data(), dA, A.size()*sizeof(float), cudaMemcpyDeviceToHost);
    std::cout << "A: \n";
    for (int i = 0; i < m; ++i) {
        for (int j = 0; j < m; ++j) {
            std::cout << A[i*m + j] << " ";
        }
        std::cout << std::endl;
    }

    cudaFree(dA);

    std::cout << "b: \n";
    cudaMemcpy(b.data(), db, (m)*sizeof(int), cudaMemcpyDeviceToHost);
    for (auto a : b) {
        std::cout << a << ",";
    }
    std::cout << std::endl;


    // --- solving!!!!

//    cusolver_status = cusolverSpScsrlsvchol(cusolver_handle, m, nnz, descrA, dcsrVal,
//                       dcsrRowPtr, dcsrColInd, db, tol, reorder, dx,
//                       &singularity);

     cusolver_status = cusolverSpScsrlsvqr(cusolver_handle, m, nnz, descrA, dcsrVal,
                        dcsrRowPtr, dcsrColInd, db, tol, reorder, dx,
                        &singularity);

    cudaDeviceSynchronize();

    std::cout << "singularity (should be -1): " << singularity << std::endl;

    std::cout << "status cusolver solving (!): " << cusolver_status << std::endl;

    cudaMemcpy(x.data(), dx, m*sizeof(float), cudaMemcpyDeviceToHost);

    // relocated these 2 lines from above to solve (2):
    cusparse_status = cusparseDestroy(cusparse_handle);
    std::cout << "status destroy cusparse handle: " << cusparse_status << std::endl;

    cusolver_status = cusolverSpDestroy(cusolver_handle);
    std::cout << "status destroy cusolver handle: " << cusolver_status << std::endl;

    for (auto a : x) {
        std::cout << a << " ";
    }
    std::cout << std::endl;



    cudaFree(db);
    cudaFree(dx);
    cudaFree(dcsrVal);
    cudaFree(dcsrColInd);
    cudaFree(dcsrRowPtr);
    cudaFree(dcooRow);

    return 0;
}

这看起来很奇怪。我开始测试我的应用程序。登录后我的重定向器工作正常，我可以看到我的记录。点击详细信息（Termine）我得到了下一个问题，这里是代码片段：

../public/images/xyz.png

我得到一个空白页面。似乎无法找到我的控制器动作。

任何人都可以帮助解决我的问题吗？

编辑：

啊哈，我切换登录并收到错误：

解析错误：语法错误，意外的T_OBJECT_OPERATOR /mnt/weba/b3/45/5543245/htdocs/cheyenne/application/controllers/AktermineController.php 在第88行

给定控制器中的第88行是一些日期广播：

./images/xyz.png

所以这对生产系统不起作用，但为什么呢？

编辑2：

我试图改为：

<td class="row_<?PHP echo $i % 2;?>">
    <a href="<?php echo $this->url(array('controller'=>'aktermine', 'action'=>'index', 'kopfnr'=>$kopf->kopfnr));?>">Termine</a>
</td>

这样可行，但我需要演员，或者我在另一篇文章中要求的一些日期选择器。

Answer 1

由于您使用的是zend框架，为什么不使用Zend_Date

$dateObj = new Zend_Date();
$dateObj->set($form->getValue('datum'),'dd/MM/yyyy');
$datum = $dateObj->toString('yyyy-MM-dd');

Answer 2

问题出在这一行：

$datum = (new DateTime($form->getValue('datum')))->format('Y-m-d');

我改变了它，如上所示。

生产服务器上找不到控制器操作

2 个答案: