diff --git a/benchmarks/cugraph/pytest-based/bench_algos.py b/benchmarks/cugraph/pytest-based/bench_algos.py index 4a87b7086ea..04407d656d7 100644 --- a/benchmarks/cugraph/pytest-based/bench_algos.py +++ b/benchmarks/cugraph/pytest-based/bench_algos.py @@ -330,8 +330,17 @@ def bench_bfs(gpubenchmark, graph): def bench_sssp(gpubenchmark, graph): + if not graph.is_weighted(): + pytest.skip("Skipping: Unweighted Graphs are not supported by SSSP") + sssp = dask_cugraph.sssp if is_graph_distributed(graph) else cugraph.sssp - start = graph.edgelist.edgelist_df["src"][0] + + start_col = graph.select_random_vertices(num_vertices=1) + if is_graph_distributed(graph): + start_col = start_col.compute() + + start = start_col.to_arrow().to_pylist()[0] + gpubenchmark(sssp, graph, start) diff --git a/benchmarks/pytest.ini b/benchmarks/pytest.ini index b3d8a8bb36c..fe7fc31b6d6 100644 --- a/benchmarks/pytest.ini +++ b/benchmarks/pytest.ini @@ -1,67 +1,67 @@ [pytest] pythonpath = - shared/python + shared/python testpaths = - cugraph/pytest_based - cugraph-service/pytest_based + cugraph/pytest_based + cugraph-service/pytest_based addopts = - --benchmark-columns="min, max, mean, stddev, outliers" + --benchmark-columns="min, max, mean, stddev, outliers" markers = - managedmem_on: RMM managed memory enabled - managedmem_off: RMM managed memory disabled - poolallocator_on: RMM pool allocator enabled - poolallocator_off: RMM pool allocator disabled - tiny: tiny datasets - small: small datasets - medium: medium datasets - large: large datasets - directed: directed datasets - undirected: undirected datasets - matrix_types: inputs are matrices - nx_types: inputs are NetowrkX Graph objects - cugraph_types: inputs are cuGraph Graph objects - sg: single-GPU - mg: multi-GPU - snmg: single-node multi-GPU - mnmg: multi-node multi-GPU - local: local cugraph - remote: cugraph-service - batch_size_100: batch size of 100 for sampling algos - batch_size_500: batch size of 500 for sampling algos - batch_size_1000: batch size of 1000 for sampling algos - batch_size_2500: batch size of 2500 for sampling algos - batch_size_5000: batch size of 5000 for sampling algos - batch_size_10000: batch size of 10000 for sampling algos - batch_size_20000: batch size of 20000 for sampling algos - batch_size_30000: batch size of 30000 for sampling algos - batch_size_40000: batch size of 40000 for sampling algos - batch_size_50000: batch size of 50000 for sampling algos - batch_size_60000: batch size of 60000 for sampling algos - batch_size_70000: batch size of 70000 for sampling algos - batch_size_80000: batch size of 80000 for sampling algos - batch_size_90000: batch size of 90000 for sampling algos - batch_size_100000: batch size of 100000 for sampling algos - num_clients_2: start 2 cugraph-service clients - num_clients_4: start 4 cugraph-service clients - num_clients_8: start 8 cugraph-service clients - num_clients_16: start 16 cugraph-service clients - num_clients_32: start 32 cugraph-service clients - fanout_10_25: fanout [10, 25] for sampling algos - fanout_5_10_15: fanout [5, 10, 15] for sampling algos - rmat_data: RMAT-generated synthetic datasets - file_data: datasets from $RAPIDS_DATASET_ROOT_DIR + managedmem_on: RMM managed memory enabled + managedmem_off: RMM managed memory disabled + poolallocator_on: RMM pool allocator enabled + poolallocator_off: RMM pool allocator disabled + tiny: tiny datasets + small: small datasets + medium: medium datasets + large: large datasets + directed: directed datasets + undirected: undirected datasets + matrix_types: inputs are matrices + nx_types: inputs are NetowrkX Graph objects + cugraph_types: inputs are cuGraph Graph objects + sg: single-GPU + mg: multi-GPU + snmg: single-node multi-GPU + mnmg: multi-node multi-GPU + local: local cugraph + remote: cugraph-service + batch_size_100: batch size of 100 for sampling algos + batch_size_500: batch size of 500 for sampling algos + batch_size_1000: batch size of 1000 for sampling algos + batch_size_2500: batch size of 2500 for sampling algos + batch_size_5000: batch size of 5000 for sampling algos + batch_size_10000: batch size of 10000 for sampling algos + batch_size_20000: batch size of 20000 for sampling algos + batch_size_30000: batch size of 30000 for sampling algos + batch_size_40000: batch size of 40000 for sampling algos + batch_size_50000: batch size of 50000 for sampling algos + batch_size_60000: batch size of 60000 for sampling algos + batch_size_70000: batch size of 70000 for sampling algos + batch_size_80000: batch size of 80000 for sampling algos + batch_size_90000: batch size of 90000 for sampling algos + batch_size_100000: batch size of 100000 for sampling algos + num_clients_2: start 2 cugraph-service clients + num_clients_4: start 4 cugraph-service clients + num_clients_8: start 8 cugraph-service clients + num_clients_16: start 16 cugraph-service clients + num_clients_32: start 32 cugraph-service clients + fanout_10_25: fanout [10, 25] for sampling algos + fanout_5_10_15: fanout [5, 10, 15] for sampling algos + rmat_data: RMAT-generated synthetic datasets + file_data: datasets from $RAPIDS_DATASET_ROOT_DIR python_classes = - Bench* - Test* + Bench* + Test* python_files = - bench_* - test_* + bench_* + test_* python_functions = - bench_* - test_* + bench_* + test_*