def gpu_sweep_col_mult(X, y):
""" X * y = X across the columns """
if type(X)==GPUArray:
gX = X
else:
gX = to_gpu(np.asarray(X, dtype=np.float32))
if type(y)==GPUArray:
gy = y
else:
gy = to_gpu(np.asarray(y, dtype=np.float32))
dims = np.asarray(X.shape, dtype=np.int32)
if devinfo.max_block_threads >= 1024:
blocksize = 32
else:
blocksize = 16
gridsize = int(dims[0] / blocksize) + 1
shared = 4*blocksize
if gX.flags.c_contiguous:
func = CUDA_Kernels.get_function("sweep_columns_mult")
else:
func = CUDA_Kernels.get_function("sweep_columns_mult_cm")
func(gX, gy, dims[0], dims[1], block=(blocksize, blocksize,1),
grid = (gridsize,1), shared = shared)
if type(y)!=GPUArray:
X = gX.get()
def gpu_apply_row_max(X):
"""
max(X) = y across the rows
returns the gpuarray, y
"""
if type(X)==GPUArray:
gX = X
else:
gX = to_gpu(np.asarray(X, dtype=np.float32))
dims = np.asarray(X.shape, dtype=np.int32)
gy = to_gpu(np.zeros(dims[0], dtype=np.float32))
giy = to_gpu(np.zeros(dims[0], dtype=np.int32))
if devinfo.max_block_threads >= 1024:
blocksize = 32
else:
blocksize = 16
gridsize = int(dims[0] / blocksize) + 1
if gX.flags.c_contiguous:
func = CUDA_Kernels.get_function("apply_rows_max")
shared = 4*blocksize*(blocksize+2) # pad for bank conflicts
else:
func = CUDA_Kernels.get_function("apply_rows_max_cm")
shared = 4*blocksize*(blocksize+1)
func(gX, gy, giy, dims[0], dims[1], block=(blocksize, blocksize,1),
grid = (gridsize,1), shared = shared)
return gy, giy
def gpu_sweep_row_div(X, y):
""" X / y = X down the rows """
if type(X)==GPUArray:
gX = X
else:
gX = to_gpu(np.asarray(X, dtype=np.float32))
if type(y)==GPUArray:
gy = y
else:
gy = to_gpu(np.asarray(y, dtype=np.float32))
dims = np.asarray(X.shape, dtype=np.int32)
if devinfo.max_block_threads >= 1024:
blocksize = 32
else:
blocksize = 16
gridsize = int(dims[0] / blocksize) + 1
shared = int(4*dims[1])
if gX.flags.c_contiguous:
func = CUDA_Kernels.get_function("sweep_rows_div")
else:
func = CUDA_Kernels.get_functions("sweep_rows_div_cm")
func(gX, gy, dims[0], dims[1], block=(blocksize, blocksize,1),
grid = (gridsize,1), shared = shared)
if type(y)!=GPUArray:
X = gX.get()
def gpu_sweep_row_diff(X, y):
""" X - y = X down the rows """
if type(X) == GPUArray:
gX = X
else:
gX = to_gpu(np.asarray(X, dtype=np.float32))
if type(y) == GPUArray:
gy = y
else:
gy = to_gpu(np.asarray(y, dtype=np.float32))
dims = np.asarray(X.shape, dtype=np.int32)
if devinfo.max_block_threads >= 1024:
blocksize = 32
else:
blocksize = 16
gridsize = int(dims[0] / blocksize) + 1
shared = int(4 * dims[1])
if gX.flags.c_contiguous:
func = CUDA_Kernels.get_function("sweep_rows_diff")
else:
func = CUDA_Kernels.get_function("sweep_rows_diff_cm")
func(gX,
gy,
dims[0],
dims[1],
block=(blocksize, blocksize, 1),
grid=(gridsize, 1),
shared=shared)
if type(y) != GPUArray:
X = gX.get()
def gpu_sweep_col_mult(X, y):
""" X * y = X across the columns """
if type(X) == GPUArray:
gX = X
else:
gX = to_gpu(np.asarray(X, dtype=np.float32))
if type(y) == GPUArray:
gy = y
else:
gy = to_gpu(np.asarray(y, dtype=np.float32))
dims = np.asarray(X.shape, dtype=np.int32)
if devinfo.max_block_threads >= 1024:
blocksize = 32
else:
blocksize = 16
gridsize = int(dims[0] / blocksize) + 1
shared = 4*blocksize
if gX.flags.c_contiguous:
func = CUDA_Kernels.get_function("sweep_columns_mult")
else:
func = CUDA_Kernels.get_function("sweep_columns_mult_cm")
func(gX, gy, dims[0], dims[1], block=(blocksize, blocksize, 1),
grid = (gridsize, 1), shared = shared)
if type(y) != GPUArray:
X = gX.get()
def gpu_apply_row_max(X):
"""
max(X) = y across the rows
returns the gpuarray, y
"""
if type(X) == GPUArray:
gX = X
else:
gX = to_gpu(np.asarray(X, dtype=np.float32))
dims = np.asarray(X.shape, dtype=np.int32)
gy = to_gpu(np.zeros(dims[0], dtype=np.float32))
giy = to_gpu(np.zeros(dims[0], dtype=np.int32))
if devinfo.max_block_threads >= 1024:
blocksize = 32
else:
blocksize = 16
gridsize = int(dims[0] / blocksize) + 1
if gX.flags.c_contiguous:
func = CUDA_Kernels.get_function("apply_rows_max")
shared = 4*blocksize*(blocksize+2) # pad for bank conflicts
else:
func = CUDA_Kernels.get_function("apply_rows_max_cm")
shared = 4*blocksize*(blocksize+1)
func(gX, gy, giy, dims[0], dims[1], block=(blocksize, blocksize, 1),
grid = (gridsize, 1), shared = shared)
return gy, giy