def node2vec_walks():
"""Simulate a random walk starting from start node."""
n = start_node_idx_ary.size
# use last entry of each walk index array to keep track of effective walk length
walk_idx_mat = np.zeros((n, walk_length + 2), dtype=np.uint32)
walk_idx_mat[:, 0] = start_node_idx_ary # initialize seeds
walk_idx_mat[:,
-1] = walk_length + 1 # set to full walk length by default
# progress bar parameters
n_checkpoints = 10
checkpoint = n / get_num_threads() // n_checkpoints
progress_bar_length = 25
private_count = 0
for i in prange(n):
# initialize first step as normal random walk
start_node_idx = walk_idx_mat[i, 0]
if has_nbrs(start_node_idx):
walk_idx_mat[i, 1] = move_forward(start_node_idx)
else:
walk_idx_mat[i, -1] = 1
continue
# start bias random walk
for j in range(2, walk_length + 1):
cur_idx = walk_idx_mat[i, j - 1]
if has_nbrs(cur_idx):
prev_idx = walk_idx_mat[i, j - 2]
walk_idx_mat[i, j] = move_forward(cur_idx, prev_idx)
else:
walk_idx_mat[i, -1] = j
break
if verbose:
# TODO: make monitoring less messy
private_count += 1
if private_count % checkpoint == 0:
progress = private_count / n * progress_bar_length * get_num_threads(
)
# manuual construct progress bar since string formatting not supported
progress_bar = '|'
for k in range(progress_bar_length):
progress_bar += '#' if k < progress else ' '
progress_bar += '|'
print(
"Thread # "
if _get_thread_id() < 10 else "Thread #",
_get_thread_id(), "progress:", progress_bar,
get_num_threads() * private_count * 10000 // n /
100, "%")
return walk_idx_mat
def test_func():
set_num_threads(mask)
x = 5000000
buf = np.empty((x, ))
for i in prange(x):
buf[i] = _get_thread_id()
return len(np.unique(buf)), get_num_threads()
def work(local_nt): # arg is value 3
tid = np.zeros(BIG)
acc = 0
set_num_threads(local_nt) # set to 3 threads
for i in prange(BIG):
acc += 1
tid[i] = _get_thread_id()
return acc, np.unique(tid)
def test_gufunc(x, out):
set_num_threads(mask)
x[:] = _get_thread_id()
out[0] = get_num_threads()
