pylibhipgraph.bfs

pylibhipgraph.bfs#

2025-05-20

2 min read time

Applies to Linux

bfs (ResourceHandle handle, _GPUGraph graph, sources, bool_t direction_optimizing, int32_t depth_limit, bool_t compute_predecessors, bool_t do_expensive_check)

Performs a Breadth-first search starting from the provided sources. Returns the distances, and predecessors if requested.

Parameters#

handle: ResourceHandle

The resource handle responsible for managing device resources that this algorithm will use

graph: SGGraph or MGGraph

The graph to operate upon

sources: cudf.Series

The vertices to start the breadth-first search from. Should match the numbering of the provided graph. All workers must have a unique set of sources. Empty sets are allowed as long as at least one worker has a source.

direction_optimizing: bool_t

Whether to treat the graph as undirected (should only be called on a symmetric graph)

depth_limit: int32_t

The depth limit at which the traversal will be stopped. If this is a negative number, the traversal will run without a depth limit.

compute_predecessors: bool_t

Whether to compute the predecessors. If left blank, -1 will be returned instead of the correct predecessor of each vertex.

do_expensive_checkbool_t

If True, performs more extensive tests on the inputs to ensure validitity, at the expense of increased run time.

Returns#

A tuple of device arrays (cupy arrays) of the form (distances, predecessors, vertices)

Examples#

>>> M = cudf.read_csv(datasets_path / 'karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = hipgraph.Graph()
>>>  G.from_cudf_edgelist(M, source='0', destination='1', edge_attr='2')
>>>
>>> handle = ResourceHandle()
>>>
>>> srcs = G.edgelist.edgelist_df['src']
>>> dsts = G.edgelist.edgelist_df['dst']
>>> weights = G.edgelist.edgelist_df['weights']
>>>
>>> sg = SGGraph(
>>>     resource_handle = handle,
>>>     graph_properties = GraphProperties(is_multigraph=G.is_multigraph()),
>>>     src_array = srcs,
>>>     dst_array = dsts,
>>>     weight_array = weights,
>>>     store_transposed=False,
>>>     renumber=False,
>>>     do_expensive_check=do_expensive_check
>>> )
>>>
>>> res = pylibhipgraph_bfs(
>>>         handle,
>>>         sg,
>>>         cudf.Series([0], dtype='int32'),
>>>         False,
>>>         10,
>>>         True,
>>>         False
>>> )
>>>
>>> distances, predecessors, vertices = res
>>>
f>>> inal_results = cudf.DataFrame({
>>>     'distance': cudf.Series(distances),
>>>     'vertex': cudf.Series(vertices),
>>>     'predecessor': cudf.Series(predecessors),
>>> })
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