def _init_thread_memory(self, dev_id: int, ctx: cuda.Context,
alloc_size: int) -> None:
'''
Single thread that initializes the memory for all the stream for a single
GPU.
'''
ctx.push()
size_per_batch = np.int32(np.ceil(alloc_size / self.num_stream))
# Initialize streams
for i in range(self.num_stream):
self.streams[dev_id].append(cuda.Stream())
for i in range(0, self.num_stream, 1):
# allocate memory on device
self.moments_device[dev_id].append(
(cuda.mem_alloc(int(SIZEOF_FLOAT * size_per_batch * 10))))
self.w_device[dev_id].append(
(cuda.mem_alloc(int(SIZEOF_FLOAT * size_per_batch * 9))))
self.x_device[dev_id].append(
(cuda.mem_alloc(int(SIZEOF_FLOAT * size_per_batch * 9))))
self.y_device[dev_id].append(
(cuda.mem_alloc(int(SIZEOF_FLOAT * size_per_batch * 9))))
# set host memory for returned output
self.c_moments[dev_id].append(
(cuda.mem_alloc(int(SIZEOF_FLOAT * size_per_batch * 7))))
self.mu[dev_id].append(
(cuda.mem_alloc(int(SIZEOF_FLOAT * size_per_batch * 3))))
self.yf[dev_id].append(
(cuda.mem_alloc(int(SIZEOF_FLOAT * size_per_batch * 3))))
self.m1[dev_id].append(
(cuda.mem_alloc(int(SIZEOF_FLOAT * size_per_batch * 5))))
self.float_value_set[dev_id](self.m1[dev_id][i],
np.float32(0),
size_per_batch,
size_per_batch,
block=self.block_size,
grid=self.grid_size,
stream=self.streams[dev_id][i])
self.float_value_set[dev_id](self.m1[dev_id][i],
np.float32(1),
size_per_batch,
np.int32(0),
block=self.block_size,
grid=self.grid_size,
stream=self.streams[dev_id][i])
self.x1[dev_id].append(
(cuda.mem_alloc(int(SIZEOF_FLOAT * size_per_batch * 3))))
self.w1[dev_id].append(
(cuda.mem_alloc(int(SIZEOF_FLOAT * size_per_batch * 3))))
self.x2[dev_id].append(
(cuda.mem_alloc(int(SIZEOF_FLOAT * size_per_batch * 3))))
self.w2[dev_id].append(
(cuda.mem_alloc(int(SIZEOF_FLOAT * size_per_batch * 3))))
ctx.synchronize()
ctx.pop()
def _set_thread_args(self, dev_id: int, ctx: cuda.Context,
moment: np.ndarray, w_out: np.ndarray,
x_out: np.ndarray, y_out: np.ndarray):
'''
Set the input moment for all the stream for a specific GPU
'''
ctx.push()
# number of input for this GPU
max_size = moment.shape[1]
# loop through the streams to set their input
for i in range(0, self.num_stream, 1):
# Size of input allocated for each stream
size_per_batch = int(np.ceil(max_size / self.num_stream))
# location on the original input array where the input to this stream starts
loc = np.int32((i) * size_per_batch)
if loc + size_per_batch > max_size:
size_per_batch = max_size - loc
self.moment_chunk_host[dev_id].append(
np.ascontiguousarray(moment[:, loc:loc + size_per_batch],
dtype=np.float32))
self.moment_chunk_host[dev_id][i] = cuda.register_host_memory(
self.moment_chunk_host[dev_id][i],
cuda.mem_host_register_flags.PORTABLE)
self.w_chunk_host[dev_id].append(
np.ascontiguousarray(
np.zeros_like(w_out[:, loc:loc + size_per_batch])))
self.w_chunk_host[dev_id][i] = cuda.register_host_memory(
self.w_chunk_host[dev_id][i],
cuda.mem_host_register_flags.PORTABLE)
self.x_chunk_host[dev_id].append(
np.ascontiguousarray(
np.zeros_like(x_out[:, loc:loc + size_per_batch])))
self.x_chunk_host[dev_id][i] = cuda.register_host_memory(
self.x_chunk_host[dev_id][i],
cuda.mem_host_register_flags.PORTABLE)
self.y_chunk_host[dev_id].append(
np.ascontiguousarray(
np.zeros_like(y_out[:, loc:loc + size_per_batch])))
self.y_chunk_host[dev_id][i] = cuda.register_host_memory(
self.y_chunk_host[dev_id][i],
cuda.mem_host_register_flags.PORTABLE)
ctx.synchronize()
ctx.pop()
