我正在尝试将CUSP集成到现有的Fortran代码中。现在,我只是试图获得基本设置推力/ CUSP从Fortran输入数组,并使用它们来构造一个CUSP矩阵(coo格式现在)。由于以下线程:unresolved-references-using-ifort-with-nvcc-and-cusp,我已经能够获得一个像C例程这样的包装器来编译到库中,并将它与Fortran代码链接起来
我可以验证Fortran是否正确地输入数组指针,这要归功于前一个线程:matrix from passed FORTRAN arrays的帮助。
不幸的是,我仍然不能让CUSP使用这些来生成和打印一个矩阵。代码和输出如下所示:
输出
$ ./fort_cusp_test
testing 1 2 3
n: 3, nnz: 9
i, row_i, col_j, val_v
0, 1, 1, 1.0000e+00
1, 1, 2, 2.0000e+00
2, 1, 3, 3.0000e+00
3, 2, 1, 4.0000e+00
4, 2, 2, 5.0000e+00
5, 2, 3, 6.0000e+00
6, 3, 1, 7.0000e+00
7, 3, 2, 8.0000e+00
8, 3, 3, 9.0000e+00
initialized row_i into thrust
initialized col_j into thrust
initialized val_v into thrust
defined CUSP integer array view for row_i and col_j
defined CUSP float array view for val_v
loaded row_i into a CUSP integer array view
loaded col_j into a CUSP integer array view
loaded val_v into a CUSP float array view
defined CUSP coo_matrix view
Built matrix A from CUSP device views
sparse matrix <3, 3> with 9 entries
libc++abi.dylib: terminating with uncaught exception of type thrust::system::system_error: invalid argument
Program received signal SIGABRT: Process abort signal.
Backtrace for this error:
#0 0x10d0fdff6
#1 0x10d0fd593
#2 0x7fff8593ff19
Abort trap: 6fort_cusp_test.f90
program fort_cuda_test
implicit none
! interface
! subroutine test_coo_mat_print_(row_i,col_j,val_v,n,nnz) bind(C)
! use, intrinsic :: ISO_C_BINDING, ONLY: C_INT,C_FLOAT
! implicit none
! integer(C_INT),value :: n, nnz
! integer(C_INT) :: row_i(:), col_j(:)
! real(C_FLOAT) :: val_v(:)
! end subroutine test_coo_mat_print_
! end interface
integer*4 n
integer*4 nnz
integer*4, target :: rowI(9),colJ(9)
real*4, target :: valV(9)
integer*4, pointer :: row_i(:)
integer*4, pointer :: col_j(:)
real*4, pointer :: val_v(:)
n = 3
nnz = 9
rowI = (/ 1, 1, 1, 2, 2, 2, 3, 3, 3/)
colJ = (/ 1, 2, 3, 1, 2, 3, 1, 2, 3/)
valV = (/ 1, 2, 3, 4, 5, 6, 7, 8, 9/)
row_i => rowI
col_j => colJ
val_v => valV
write(*,*) "testing 1 2 3"
call test_coo_mat_print (rowI,colJ,valV,n,nnz)
end program fort_cuda_testcusp_runner.cu
#include <stdio.h>
#include <cusp/coo_matrix.h>
#include <iostream>
// #include <cusp/krylov/cg.h>
#include <cusp/print.h>
#if defined(__cplusplus)
extern "C" {
#endif
void test_coo_mat_print_(int * row_i, int * col_j, float * val_v, int * N, int * NNZ ) {
int n, nnz;
n = *N;
nnz = *NNZ;
printf("n: %d, nnz: %d\n",n,nnz);
printf("%6s, %6s, %6s, %12s \n","i","row_i","col_j","val_v");
for(int i=0;i<n;i++) {
printf("%6d, %6d, %6d, %12.4e\n",i,row_i[i],col_j[i],val_v[i]);
}
//if ( false ) {
//wrap raw input pointers with thrust::device_ptr
thrust::device_ptr<int> wrapped_device_I(row_i);
printf("initialized row_i into thrust\n");
thrust::device_ptr<int> wrapped_device_J(col_j);
printf("initialized col_j into thrust\n");
thrust::device_ptr<float> wrapped_device_V(val_v);
printf("initialized val_v into thrust\n");
//use array1d_view to wrap individual arrays
typedef typename cusp::array1d_view< thrust::device_ptr<int> > DeviceIndexArrayView;
printf("defined CUSP integer array view for row_i and col_j\n");
typedef typename cusp::array1d_view< thrust::device_ptr<float> > DeviceValueArrayView;
printf("defined CUSP float array view for val_v\n");
DeviceIndexArrayView row_indices(wrapped_device_I, wrapped_device_I + nnz);
printf("loaded row_i into a CUSP integer array view\n");
DeviceIndexArrayView column_indices(wrapped_device_J, wrapped_device_J + nnz);
printf("loaded col_j into a CUSP integer array view\n");
DeviceValueArrayView values(wrapped_device_V, wrapped_device_V + nnz);
printf("loaded val_v into a CUSP float array view\n");
//combine array1d_views into coo_matrix_view
typedef cusp::coo_matrix_view<DeviceIndexArrayView,DeviceIndexArrayView,DeviceValueArrayView> DeviceView;
printf("defined CUSP coo_matrix view\n");
//construct coo_matrix_view from array1d_views
DeviceView A(n,n,nnz,row_indices,column_indices,values);
printf("Built matrix A from CUSP device views\n");
cusp::print(A);
printf("Printed matrix A\n");
//}
}
#if defined(__cplusplus)
}
#endifMakefile
Test:
nvcc -Xcompiler="-fPIC" -shared cusp_runner.cu -o cusp_runner.so -I/Developer/NVIDIA/CUDA-6.5/include/cusp
gfortran -c fort_cusp_test.f90
gfortran fort_cusp_test.o cusp_runner.so -L/Developer/NVIDIA/CUDA-6.5/lib -lcudart -o fort_cusp_test
clean:
rm *.o *.so fort_cusp_testcusp_runner.cu的功能版本
#include <stdio.h>
#include <cusp/coo_matrix.h>
#include <iostream>
// #include <cusp/krylov/cg.h>
#include <cusp/print.h>
#if defined(__cplusplus)
extern "C" {
#endif
void test_coo_mat_print_(int * row_i, int * col_j, float * val_v, int * N, int * NNZ ) {
int n, nnz;
n = *N;
nnz = *NNZ;
printf("n: %d, nnz: %d\n",n,nnz);
printf("printing input (row_i, col_j, val_v)\n");
printf("%6s, %6s, %6s, %12s \n","i","row_i","col_j","val_v");
for(int i=0;i<nnz;i++) {
printf("%6d, %6d, %6d, %12.4e\n",i,row_i[i],col_j[i],val_v[i]);
}
printf("initializing thrust device vectors\n");
thrust::device_vector<int> device_I(row_i,row_i+nnz);
printf("device_I initialized\n");
thrust::device_vector<int> device_J(col_j,col_j+nnz);
printf("device_J initialized\n");
thrust::device_vector<float> device_V(val_v,val_v+nnz);
printf("device_V initialized\n");
cusp::coo_matrix<int, float, cusp::device_memory> A(n,n,nnz);
printf("initialized empty CUSP coo_matrix on device\n");
A.row_indices = device_I;
printf("loaded device_I into A.row_indices\n");
A.column_indices = device_J;
printf("loaded device_J into A.column_indices\n");
A.values = device_V;
printf("loaded device_V into A.values\n");
cusp::print(A);
printf("Printed matrix A\n");
//}
}
#if defined(__cplusplus)
}
#endif发布于 2015-08-18 05:40:44
处理指针的推力/CUSP侧代码完全不正确。这是:
thrust::device_ptr<int> wrapped_device_I(row_i);不会像你想的那样做。实际上,您所做的是将主机地址转换为设备地址。除非您正在使用CUDA托管内存,否则这是非法的,而且我在此代码中没有看到这方面的证据。您要做的是在启动之前分配内存并将Fortran数组复制到GPU。做以下事情:
thrust::device_ptr<int> wrapped_device_I = thrust::device_malloc<int>(nnz);
thrust::copy(row_i, row_i + nnz, wrapped_device_I);免责声明:完全未经测试,自行使用
对于每个COO向量。但是,我建议将test_coo_mat_print_的GPU设置部分的大部分代码替换为thrust::vector实例。除了更容易使用之外,当内存超出作用域时,您还可以获得空闲内存释放,这样工程内存泄漏的可能性就小得多。所以,就像:
thrust::device_vector<int> device_I(row_i, row_i + nnz);在一个电话里处理所有的事情。
作为最后的提示,如果您正在开发多语言代码库,那么设计它们时,每种语言中的代码都是完全独立的,并且有自己的本机测试代码。如果在本例中这样做,您就会发现C++部分的工作并不独立于您遇到的任何Fortran问题。这样可以使调试更加简单。
https://stackoverflow.com/questions/32058857
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