pynufft_gpu.py 文件源码

def precompute(self):

#         CSR_W = cuda_cffi.cusparse.CSR.to_CSR(self.st['W_gpu'],diag_type=True)

#         Dia_W_cpu = scipy.sparse.dia_matrix( (self.st['M'], self.st['M']),dtype=dtype)
#         Dia_W_cpu = scipy.sparse.dia_matrix( ( self.st['W'], 0 ), shape=(self.st['M'], self.st['M']) )
#         Dia_W_cpu = scipy.sparse.diags(self.st['W'], format="csr", dtype=dtype)
#         CSR_W = cuda_cffi.cusparse.CSR.to_CSR(Dia_W_cpu)


        self.st['pHp_gpu'] = self.CSRH.gemm(self.CSR)
        self.st['pHp']=self.st['pHp_gpu'].get()
        print('untrimmed',self.st['pHp'].nnz)
        self.truncate_selfadjoint(1e-5)
        print('trimmed', self.st['pHp'].nnz)
        self.st['pHp_gpu'] = cuda_cffi.cusparse.CSR.to_CSR(self.st['pHp'])
#         self.st['pHWp_gpu'] = self.CSR.conj().gemm(CSR_W,transA=cuda_cffi.cusparse.CUSPARSE_OPERATION_TRANSPOSE)
#         self.st['pHWp_gpu'] = self.st['pHWp_gpu'].gemm(self.CSR, transA=cuda_cffi.cusparse.CUSPARSE_OPERATION_NON_TRANSPOSE)