def func_to_benchmark():
for sv in start_vertex:
cugraph_df = cugraph.bfs_edges(G, sv, depth_limit=depth_limit)
all_cugraph_distances.append(cugraph_df)
def func_to_benchmark():
for sv in start_vertex:
cugraph_df = cugraph.bfs_edges(
G, sv, return_sp_counter=return_sp_counter)
all_cugraph_distances.append(cugraph_df)
def _compare_bfs(G, Gnx, source):
df = cugraph.bfs_edges(G, source, return_sp_counter=False)
if isinstance(df, pandas.DataFrame):
df = cudf.from_pandas(df)
# This call should only contain 3 columns:
# 'vertex', 'distance', 'predecessor'
# It also confirms wether or not 'sp_counter' has been created by the call
# 'sp_counter' triggers atomic operations in BFS, thus we want to make
# sure that it was not the case
# NOTE: 'predecessor' is always returned while the C++ function allows to
# pass a nullptr
assert len(df.columns) == 3, ("The result of the BFS has an invalid "
"number of columns")
cu_distances = {
vertex: dist
for vertex, dist in zip(df["vertex"].to_array(),
df["distance"].to_array())
}
cu_predecessors = {
vertex: dist
for vertex, dist in zip(df["vertex"].to_array(),
df["predecessor"].to_array())
}
nx_distances = nx.single_source_shortest_path_length(Gnx, source)
# FIXME: The following only verifies vertices that were reached
# by cugraph's BFS.
# We assume that the distances are given back as integers in BFS
# max_val = np.iinfo(df['distance'].dtype).max
# Unreached vertices have a distance of max_val
missing_vertex_error = 0
distance_mismatch_error = 0
invalid_predecessor_error = 0
for vertex in nx_distances:
if vertex in cu_distances:
result = cu_distances[vertex]
expected = nx_distances[vertex]
if result != expected:
print("[ERR] Mismatch on distances: "
"vid = {}, cugraph = {}, nx = {}".format(
vertex, result, expected))
distance_mismatch_error += 1
if vertex not in cu_predecessors:
missing_vertex_error += 1
else:
pred = cu_predecessors[vertex]
if vertex != source and pred not in nx_distances:
invalid_predecessor_error += 1
else:
# The graph is unweighted thus, predecessors are 1 away
if vertex != source and (
(nx_distances[pred] + 1 != cu_distances[vertex])):
print("[ERR] Invalid on predecessors: "
"vid = {}, cugraph = {}".format(vertex, pred))
invalid_predecessor_error += 1
else:
missing_vertex_error += 1
assert missing_vertex_error == 0, "There are missing vertices"
assert distance_mismatch_error == 0, "There are invalid distances"
assert invalid_predecessor_error == 0, "There are invalid predecessors"