Python 使用PyCUDA接口cuSOLVER稀疏

我正在尝试使用PyCUDA连接稀疏cuSOLVER例程cusolverSpDcsrlsvqr（）（>=CUDA 7.0），但遇到了一些困难： 我尝试过用scikits cuda（）封装密集的cuSolver例程的方式来封装这些方法

但是，调用cusolverSpDcsrlsvqr（）函数时，代码因分段错误而崩溃。 使用cuda gdb进行调试（

cuda gdb--args python-m pycuda.debug test.py；run；bt

）会产生以下堆栈跟踪

#0 0x00007fffd9e3b71a位于/usr/local/cuda/lib64/libcusolver.so中的cusolverSpXcsrissymHost（）中 #来自/usr/local/cuda/lib64/libcusolver.so的hsolverXcsrqr_zeroppivot（）中的1 0x00007fffd9df5237 #来自/usr/local/cuda/lib64/libcusolver.so的hsolverXcsrqr\u analysis\u coletree（）中的2 0x00007fffd9e0c764 #来自/usr/local/cuda/lib64/libcusolver.so的cusolverXcsrqr_分析（）中的3 0x00007fffd9f160a0 #来自/usr/local/cuda/lib64/libcusolver.so的cusolverSpScsrlsvqr（）中的4 0x00007fffd9f28d78

这很奇怪，因为我不调用cusolverSpScsrlsvqr（），也不认为它应该调用主机函数（cusolverSpXcsrissymhost）

这就是我要说的代码-感谢您的帮助：

# ### Interface cuSOLVER PyCUDA


import pycuda.gpuarray as gpuarray
import pycuda.driver as cuda
import pycuda.autoinit
import numpy as np
import scipy.sparse as sp
import ctypes


# #### wrap the cuSOLVER cusolverSpDcsrlsvqr() using ctypes

# cuSparse
_libcusparse = ctypes.cdll.LoadLibrary('libcusparse.so')

class cusparseMatDescr_t(ctypes.Structure):
    _fields_ = [
        ('MatrixType', ctypes.c_int),
        ('FillMode', ctypes.c_int),
        ('DiagType', ctypes.c_int),
        ('IndexBase', ctypes.c_int)
        ]
_libcusparse.cusparseCreate.restype = int
_libcusparse.cusparseCreate.argtypes = [ctypes.c_void_p]

_libcusparse.cusparseDestroy.restype = int
_libcusparse.cusparseDestroy.argtypes = [ctypes.c_void_p]

_libcusparse.cusparseCreateMatDescr.restype = int
_libcusparse.cusparseCreateMatDescr.argtypes = [ctypes.c_void_p]


# cuSOLVER
_libcusolver = ctypes.cdll.LoadLibrary('libcusolver.so')



_libcusolver.cusolverSpCreate.restype = int
_libcusolver.cusolverSpCreate.argtypes = [ctypes.c_void_p]

_libcusolver.cusolverSpDestroy.restype = int
_libcusolver.cusolverSpDestroy.argtypes = [ctypes.c_void_p]



_libcusolver.cusolverSpDcsrlsvqr.restype = int
_libcusolver.cusolverSpDcsrlsvqr.argtypes= [ctypes.c_void_p,
                                            ctypes.c_int,
                                            ctypes.c_int,
                                            cusparseMatDescr_t,
                                            ctypes.c_void_p,
                                            ctypes.c_void_p,
                                            ctypes.c_void_p,
                                            ctypes.c_void_p,
                                            ctypes.c_double,
                                            ctypes.c_int,
                                            ctypes.c_void_p,
                                            ctypes.c_void_p]


#### Prepare the matrix and parameters, copy to Device via gpuarray

# coo to csr
val = np.arange(1,5,dtype=np.float64)
col = np.arange(0,4,dtype=np.int32)
row = np.arange(0,4,dtype=np.int32)
A = sp.coo_matrix((val,(row,col))).todense()
Acsr = sp.csr_matrix(A)
b = np.ones(4)
x = np.empty(4)
print('A:' + str(A))
print('b: ' + str(b))


dcsrVal = gpuarray.to_gpu(Acsr.data)
dcsrColInd = gpuarray.to_gpu(Acsr.indices)
dcsrIndPtr = gpuarray.to_gpu(Acsr.indptr)
dx = gpuarray.to_gpu(x)
db = gpuarray.to_gpu(b)
m = ctypes.c_int(4)
nnz = ctypes.c_int(4)
descrA = cusparseMatDescr_t()
reorder = ctypes.c_int(0)
tol = ctypes.c_double(1e-10)
singularity = ctypes.c_int(99)


#create cusparse handle
_cusp_handle = ctypes.c_void_p()
status = _libcusparse.cusparseCreate(ctypes.byref(_cusp_handle))
print('status: ' + str(status))
cusp_handle = _cusp_handle.value

#create MatDescriptor
status = _libcusparse.cusparseCreateMatDescr(ctypes.byref(descrA))
print('status: ' + str(status))

#create cusolver handle
_cuso_handle = ctypes.c_void_p()
status = _libcusolver.cusolverSpCreate(ctypes.byref(_cuso_handle))
print('status: ' + str(status))
cuso_handle = _cuso_handle.value



print('cusp handle: ' + str(cusp_handle))
print('cuso handle: ' + str(cuso_handle))


### Call solver
_libcusolver.cusolverSpDcsrlsvqr(cuso_handle,
                                 m,
                                 nnz,
                                 descrA,
                                 int(dcsrVal.gpudata),
                                 int(dcsrIndPtr.gpudata),
                                 int(dcsrColInd.gpudata),
                                 int(db.gpudata),
                                 tol,
                                 reorder,
                                 int(dx.gpudata),
                                 ctypes.byref(singularity))

# destroy handles
status = _libcusolver.cusolverSpDestroy(cuso_handle)
print('status: ' + str(status))
status = _libcusparse.cusparseDestroy(cusp_handle)
print('status: ' + str(status))

---

将

descrA

设置为

ctypes.c\u void\u p（）

并将

cusolverSpDcsrlsvqr

包装中的

cusparseMatDescr\u t

替换为

ctypes.c\u void\u p

应该可以解决问题