def forward(self, matrix1, matrix2):
with torch.cuda.device_of(matrix1):
dim1, dim2 = matrix1.size()
dim2, dim3 = matrix2.size()
output = matrix1.new(dim1, dim3)
handle = torch.cuda.current_blas_handle()
stream = torch.cuda.current_stream()
cublas.cublasSetStream(handle, stream)
if isinstance(matrix1, torch.cuda.FloatTensor):
cublas.cublasSgemm(handle, 'n', 'n', dim3, dim1, dim2, 1,
matrix2.data_ptr(), dim3,
matrix1.data_ptr(), dim2, 0,
output.data_ptr(), dim3)
elif isinstance(matrix1, torch.cuda.DoubleTensor):
cublas.cublasDgemm(handle, 'n', 'n', dim3, dim1, dim2, 1,
matrix2.data_ptr(), dim3,
matrix1.data_ptr(), dim2, 0,
output.data_ptr(), dim3)
self.save_for_backward(matrix1, matrix2)
return output
def mult_BLAS(): alpha = np.float64(1.0) # no prefactor beta = np.float64(0.0) # C matrix is not involved so beta = 0.0 #m, k, n = ud.basis_size, ud.basis_size, ud.basis_size**2 t0 = time.clock() for a in range(100): cublas.cublasDgemm(handle = cublas.cublasCreate(), transa = 'n', transb = 'n', m = ud.i, n = ud.j_k, k = ud.i_prime, lda = ud.i, ldb = ud.i_prime, ldc = ud.i, alpha = alpha, beta = beta, A = T_gpu.gpudata, B = v_x_gpu.gpudata, C = U_x_gpu.gpudata, ) cublas.cublasDgemm(handle = cublas.cublasCreate(), transa = 'n', transb = 'n', m = ud.i, n = ud.j_k, k = ud.i_prime, lda = ud.i, ldb = ud.i_prime, ldc = ud.i, alpha = alpha, beta = beta, A = T_gpu.gpudata, B = v_y_gpu.gpudata, C = U_y_gpu.gpudata, ) cublas.cublasDgemm(handle = cublas.cublasCreate(), transa = 'n', transb = 'n', m = ud.i, n = ud.j_k, k = ud.i_prime, lda = ud.i, ldb = ud.i_prime, ldc = ud.i_prime, alpha = alpha, beta = beta, A = T_gpu.gpudata, B = v_z_gpu.gpudata, C = U_z_gpu.gpudata, ) '''cublas.cublasDgemm(handle = cublas.cublasCreate(), transa = 'n', transb = 'n', m = ud.i, n = ud.j_k, k = ud.i_prime, lda = ud.i, ldb = ud.i_prime, ldc = ud.i, alpha = alpha, beta = beta, A = pot_gpu.gpudata, B = v_x_gpu.gpudata, C = potential_gpu.gpudata, )''' print(time.clock() - t0, "mult_BLAS timer") return
def diag_gpu(A, v1):
# handle
current_handle = cublas.cublasCreate()
m = A.shape[0]
Q = np.zeros((m, m), dtype=np.float64)
# Q[0, :] = 0.0 # implied
Q[1, :] = v1.copy()
beta = np.zeros(m, dtype=np.float64)
alpha = np.zeros(m, dtype=np.float64)
# move data onto the GPU
A_gpu = gpuarray.to_gpu(A)
Q_gpu = gpuarray.to_gpu(Q)
beta_gpu = gpuarray.to_gpu(beta)
alpha_gpu = gpuarray.to_gpu(alpha)
w = gpuarray.zeros(m, dtype=np.float64)
# we define three kernels for simple arithmetic
w_scale = ElementwiseKernel(
arguments="double *w, double *alpha, double *beta, double *Q1, double *Q2, int loop_index",
operation="w[i] = w[i] - (alpha[loop_index] * Q1[i]) - (beta[loop_index] * Q2[i])",
name="element_wise_w_building")
# using -= to do inplace subtraction gives an incorrect answer
norm_krnl = ReductionKernel(np.float64, neutral="0.0", reduce_expr="a+b",
map_expr="x[i]*x[i]", arguments="double *x")
ediv = ElementwiseKernel(
arguments="double *a, double *b, double *c, int loop_index",
operation="a[i] = b[i] / c[loop_index+1]",
name="element_wise_division")
# the name must not have spaces!!!!
for i in range(1, m-1):
cublas.cublasDgemv(handle = current_handle, trans = 'T',
m = m, n = m, # Hermitian matrix
alpha = 1.0,
beta = 0.0,
A = A_gpu.gpudata,
lda = m,
x = Q_gpu[i, :].gpudata,
incx = 1,
y = w.gpudata,
incy = 1,
)
cublas.cublasDgemm(handle = current_handle,
transa = 'n', transb = 'n',
m = 1, n = 1, k = m,
lda = 1, ldb = m, ldc = 1,
alpha = 1.0, beta = 0.0,
A = w.gpudata,
B = Q_gpu[i, :].gpudata,
C = alpha_gpu[i].gpudata)
w_scale(w, alpha_gpu, beta_gpu, Q_gpu[i, :], Q_gpu[i-1, :], i)
beta_gpu[i+1] = cumath.sqrt(norm_krnl(w))
ediv(Q_gpu[i+1, :], w, beta_gpu, i)
# end of loop
# last 2 steps
cublas.cublasDgemv(handle = current_handle, trans = 'T',
m = m, n = m, # Hermitian matrix
alpha = 1.0,
beta = 0.0,
A = A_gpu.gpudata,
lda = m,
x = Q_gpu[-1, :].gpudata,
incx = 1,
y = w.gpudata,
incy = 1,)
cublas.cublasDgemm(handle = current_handle,
transa = 'n', transb = 'n',
m = 1, n = 1, k = m,
lda = 1, ldb = m, ldc = 1,
alpha = 1.0, beta = 0.0,
A = w.gpudata,
B = Q_gpu[-1, :].gpudata,
C = alpha_gpu[-1].gpudata)
# retrive the alpha's and betas
alpha_cpu = alpha_gpu.get()
beta_cpu = beta_gpu.get()
print("GPU: ", alpha_cpu, beta_cpu, sep="\n\n")
# make tridiagonal matrix out of alpha and B
# Tri = np.zeros(matrix_size)
return
# allocate space on gpu for results U_x_gpu = gpuarray.zeros((basis_size, basis_size*basis_size), np.float64) # an empty matrix of the right size U_y_gpu = gpuarray.zeros((basis_size, basis_size*basis_size), np.float64) # an empty matrix of the right size U_z_gpu = gpuarray.zeros((basis_size, basis_size*basis_size), np.float64) # an empty matrix of the right size #m, k, n = ud.basis_size, ud.basis_size, ud.basis_size**2 for basis in range(BASIS_SIZE): prepare_gpu(basis) # set it up i, j_k, i_prime = basis_size, basis_size*basis_size, basis_size initial_time = time.clock() for num_iter in range(ITERATIONS): cublas.cublasDgemm(handle = cublas.cublasCreate(), transa = 'n', transb = 'n', m = i, n = j_k, k = i_prime, lda = i, ldb = i_prime, ldc = i, alpha = ud.alpha, beta = ud.beta, A = T_gpu.gpudata, B = v_x_gpu.gpudata, C = U_x_gpu.gpudata, ) cublas.cublasDgemm(handle = cublas.cublasCreate(), transa = 'n', transb = 'n', m = i, n = j_k, k = i_prime, lda = i, ldb = i_prime, ldc = i, alpha = ud.alpha, beta = ud.beta, A = T_gpu.gpudata, B = v_y_gpu.gpudata, C = U_y_gpu.gpudata, ) cublas.cublasDgemm(handle = cublas.cublasCreate(), transa = 'n', transb = 'n', m = i, n = j_k, k = i_prime, lda = i, ldb = i_prime, ldc = i,
import pycuda.autoinit
import pycuda.gpuarray as gpuarray
import numpy as np
import skcuda.cublas as cublas
A = np.array(([1, 2, 3], [4, 5, 6]), order='F').astype(np.float64)
B = np.array(([7, 8, 1, 5], [9, 10, 0, 9], [11, 12, 5, 5]),
order='F').astype(np.float64)
A_gpu = gpuarray.to_gpu(A)
B_gpu = gpuarray.to_gpu(B)
m, k = A_gpu.shape
k, n = B_gpu.shape
C_gpu = gpuarray.empty((m, n), np.float64)
alpha = np.float64(1.0)
beta = np.float64(0.0)
cublas_handle = cublas.cublasCreate()
cublas.cublasDgemm(cublas_handle, 'n', 'n', m, n, k, alpha, A_gpu.gpudata, m,
B_gpu.gpudata, k, beta, C_gpu.gpudata, m)
cublas.cublasDestroy(cublas_handle)
C_gpu = C_gpu.reshape(C_gpu.shape, order='F')
print(np.dot(A, B))
print(C_gpu)
