使用英特尔 oneAPI MKL 执行具有密集向量乘法的稀疏矩阵

Question

我正在开发一个程序，大量使用 Armadillo 库。我有 10.8.2 版本，链接到 Intel oneAPI MKL 2022.0.2。在某些时候，我需要执行许多稀疏矩阵乘以密集向量乘法，两者都是使用犰狳结构定义的。我发现这一点可能是一个瓶颈，并且很好奇是否用 MKL 中的“基本”稀疏 CBLAS 例程替换 Armadillo 乘法（mkl_sparse_d_mv) would speed things up. But in order to do so, I need to convert from Armadillo's SpMat to something that MKL understands. As per Armadillo docs, sparse matrices are stored in CSC 格式，所以我尝试过 mkl_sparse_d_create_csc。我的尝试如下：

#include <iostream>
#include <armadillo>
#include "mkl.h"

int main()
{

    arma::umat locations = {{0, 0, 1, 3, 2},{0, 1, 0, 2, 3}};
    // arma::vec vals = {0.5, 2.5, 2.5, 4.5, 4.5};
    arma::vec vals = {0.5, 2.5, 3.5, 4.5, 5.5};
    arma::sp_mat X(locations, vals);
    std::cout << "X = \n" << arma::mat(X) << std::endl;
    arma::vec v = {1,1,1,1};
    arma::vec v2;
    v2.resize(4);

    std::cout << "v = \n" << v << std::endl;
    std::cout << "X * v = \n" << X * v << std::endl;

    MKL_INT *cols_beg = static_cast<MKL_INT *>(mkl_malloc(X.n_cols * sizeof(MKL_INT), 64));
    MKL_INT *cols_end = static_cast<MKL_INT *>(mkl_malloc(X.n_cols * sizeof(MKL_INT), 64));
    MKL_INT *row_idx = static_cast<MKL_INT *>(mkl_malloc(X.n_nonzero * sizeof(MKL_INT), 64));
    double *values = static_cast<double *>(mkl_malloc(X.n_nonzero * sizeof(double), 64));

    for (MKL_INT i = 0; i < X.n_cols; i++)
    {
        cols_beg[i] = static_cast<MKL_INT>(X.col_ptrs[i]);
        cols_end[i] = static_cast<MKL_INT>((--X.end_col(i)).pos());
        std::cout << cols_beg[i] << " --- " << cols_end[i] << std::endl;
    }
    std::cout << std::endl;
    for (MKL_INT i = 0; i < X.n_nonzero; i++)
    {
        row_idx[i] = static_cast<MKL_INT>(X.row_indices[i]);
        values[i] = X.values[i];
        std::cout << row_idx[i] << " --- " << values[i] << std::endl;
    }
    std::cout << std::endl;
    sparse_matrix_t X_mkl = NULL;
    sparse_status_t res = mkl_sparse_d_create_csc(&X_mkl, SPARSE_INDEX_BASE_ZERO,
                                                  X.n_rows, X.n_cols, cols_beg, cols_end, row_idx, values);

    if(res == SPARSE_STATUS_SUCCESS) std::cout << "Constructed mkl representation of X" << std::endl;
    matrix_descr dsc;
    dsc.type = SPARSE_MATRIX_TYPE_GENERAL;

    sparse_status_t stat =  mkl_sparse_d_mv(SPARSE_OPERATION_NON_TRANSPOSE, 1.0, X_mkl, dsc, v.memptr(), 0.0, v2.memptr());
    std::cout << "Multiplication status = " << stat << std::endl;
    if(stat == SPARSE_STATUS_SUCCESS) 
    {
        std::cout << "Calculated X*v via mkl" << std::endl;
        std::cout << v2;
    }

    mkl_free(cols_beg);
    mkl_free(cols_end);
    mkl_free(row_idx);
    mkl_free(values);
    mkl_sparse_destroy(X_mkl);

    return 0;
}

我正在编译此代码（在 Link Line Advisor 的帮助下） icpc -g testing.cpp -o intel_testing.out -DARMA_ALLOW_FAKE_GCC -O3 -xhost -Wall -Wextra -L${MKLROOT}/lib/intel64 -liomp5 -lpthread -lm -DMKL_ILP64 -qmkl=parallel -larmadillo 在 Pop!_OS 21.10 上。它编译和运行没有任何问题。输出结果如下：

X = 
   0.5000   2.5000        0        0
   3.5000        0        0        0
        0        0        0   5.5000
        0        0   4.5000        0

v = 
   1.0000
   1.0000
   1.0000
   1.0000

X * v = 
   3.0000
   3.5000
   5.5000
   4.5000

0 --- 1
2 --- 2
3 --- 3
4 --- 4

0 --- 0.5
1 --- 3.5
0 --- 2.5
3 --- 4.5
2 --- 5.5

Constructed mkl representation of X
Multiplication status = 0
Calculated X*v via mkl
   0.5000
        0
        0
        0

我们可以看到，Armadillo 的乘法结果是正确的，而 MKL 的结果是错误的。我的问题是：我是不是哪里弄错了？还是MKL有问题？。我当然怀疑前者，但是在花费了大量时间之后，什么也找不到。任何帮助将不胜感激！

编辑

根据 CJR 和 Vidyalatha_Intel 的建议，我已将 col_end 更改为

 cols_end[i] = static_cast<MKL_INT>((X.end_col(i)).pos());

现在的结果是

X = 
   0.5000   2.5000        0        0
   3.5000        0        0        0
        0        0        0   5.5000
        0        0   4.5000        0

v = 
   1.0000
   1.0000
   1.0000
   1.0000

X * v = 
   3.0000
   3.5000
   5.5000
   4.5000

0 --- 2
2 --- 3
3 --- 4
4 --- 5

0 --- 0.5
1 --- 3.5
0 --- 2.5
3 --- 4.5
2 --- 5.5

Constructed mkl representation of X
Multiplication status = 0
Calculated X*v via mkl
   4.0000
   2.5000
        0
        0

col_end确实是提示的2,3,4,5，结果还是错误

Answer 1

是的，正如 CJR 所指出的，cols_end 数组不正确。它们应该被索引为 2,3,4,5。请参阅有关函数参数的文档 mkl_sparse_d_create_csc

cols_end:

This array contains col indices, such that cols_end[i] - ind - 1 is the last index of col i in the arrays values and row_indx. ind takes 0 for zero-based indexing and 1 for one-based indexing.

https://www.intel.com/content/www/us/en/develop/documentation/onemkl-developer-reference-c/top/blas-and-sparse-blas-routines/inspector-executor-sparse-blas-routines/matrix-manipulation-routines/mkl-sparse-create-csc.html

更改此行

cols_end[i] = static_cast<MKL_INT>((--X.end_col(i)).pos());

到

cols_end[i] = static_cast<MKL_INT>((X.end_col(i)).pos());

现在重新编译运行代码。我已经对其进行了测试，它显示了正确的结果。带有结果和编译命令的图像

使用英特尔 oneAPI MKL 执行具有密集向量乘法的稀疏矩阵

Using Intel oneAPI MKL to perform sparse matrix with dense vector multiplication

c++

sparse-matrix

armadillo

intel-mkl

编辑