def gemm_v2():
"""
Let GEMM transpose the input matrices so that they can be in C order,
originally. Note that the output matrix is still in Fortran array.
The string arguments in gemm tells it to apply transformation on the input
matrices.
See argument description in:
http://docs.continuum.io/accelerate/cublas#blas-level-2
"""
print("Version 2".center(80, '='))
# Prepare arrays for input
A = np.array(np.arange(N**2, dtype=np.float32).reshape(N, N))
B = np.array(np.arange(N) + 10, dtype=A.dtype)
D = np.zeros_like(A, order='F')
# NumPy
start = timer()
E = np.dot(A, np.diag(B))
numpy_time = timer() - start
print("Numpy took %f seconds" % numpy_time)
# cuBLAS
blas = Blas()
start = timer()
blas.gemm('T', 'T', N, N, N, 1.0, A, np.diag(B), 1.0, D)
cuda_time = timer() - start
print("CUBLAS took %f seconds" % cuda_time)
diff = np.abs(D - E)
print("Maximum error %f" % np.max(diff))
def gemm_v1():
'''
Note that all arrays are in Fortran order.
'''
print("Version 1".center(80, '='))
# Prepare arrays for input
A = np.array(np.arange(N**2, dtype=np.float32).reshape(N, N), order='F')
B = np.array(np.arange(N) + 10, dtype=A.dtype, order='F')
D = np.zeros_like(A, order='F')
# NumPy
start = timer()
E = np.dot(A, np.diag(B))
numpy_time = timer() - start
print("Numpy took %f seconds" % numpy_time)
# cuBLAS
blas = Blas()
start = timer()
blas.gemm('N', 'N', N, N, N, 1.0, A, np.diag(B), 1.0, D)
cuda_time = timer() - start
print("CUBLAS took %f seconds" % cuda_time)
diff = np.abs(D - E)
print("Maximum error %f" % np.max(diff))
def gemm_v1():
'''
Note that all arrays are in Fortran order.
'''
print("Version 1".center(80, '='))
# Prepare arrays for input
A = np.array(np.arange(N ** 2, dtype=np.float32).reshape(N, N), order='F')
B = np.array(np.arange(N) + 10, dtype=A.dtype, order='F')
D = np.zeros_like(A, order='F')
# NumPy
start = timer()
E = np.dot(A, np.diag(B))
numpy_time = timer() - start
print("Numpy took %f seconds" % numpy_time)
# cuBLAS
blas = Blas()
start = timer()
blas.gemm('N', 'N', N, N, N, 1.0, A, np.diag(B), 1.0, D)
cuda_time = timer() - start
print("CUBLAS took %f seconds" % cuda_time)
diff = np.abs(D - E)
print("Maximum error %f" % np.max(diff))
def gemm_v2():
"""
Let GEMM transpose the input matrices so that they can be in C order,
originally. Note that the output matrix is still in Fortran array.
The string arguments in gemm tells it to apply transformation on the input
matrices.
See argument description in:
http://docs.continuum.io/accelerate/cublas#blas-level-2
"""
print("Version 2".center(80, '='))
# Prepare arrays for input
A = np.array(np.arange(N ** 2, dtype=np.float32).reshape(N, N))
B = np.array(np.arange(N) + 10, dtype=A.dtype)
D = np.zeros_like(A, order='F')
# NumPy
start = timer()
E = np.dot(A, np.diag(B))
numpy_time = timer() - start
print("Numpy took %f seconds" % numpy_time)
# cuBLAS
blas = Blas()
start = timer()
blas.gemm('T', 'T', N, N, N, 1.0, A, np.diag(B), 1.0, D)
cuda_time = timer() - start
print("CUBLAS took %f seconds" % cuda_time)
diff = np.abs(D - E)
print("Maximum error %f" % np.max(diff))
def gemm(A, B, dD):
N = A.shape[0] # square matrices
'''
Note that all arrays are in Fortran order.
'''
# cuBLAS
blas = Blas()
start = timer()
blas.gemm('N', 'N', N, N, N, 1.0, A, B, 1.0, dD)
cuda_time = timer() - start
D = dD.copy_to_host()
print("CUBLAS took %f seconds" % cuda_time)
diff = np.abs(D - E)
print("Maximum error %f" % np.max(diff))
return D
