[FEA] Optimize cugraph.uniform_neighbor_sample

[VibhuJawa]

Describe the solution you'd like and any additional context

We should optimize cugraph.uniform_neighbor_sample.

import cudf
import cugraph

df = cudf.read_parquet('/datasets/graph_nn/ogbn_products.parquet')
G = cugraph.Graph(directed=True)
G.from_cudf_edgelist(df,source='src',destination='dst',edge_attr='weight')
batch = G.nodes()[0:4096]

%%timeit 

sampled_g_cugraph = cugraph.uniform_neighbor_sample(G,
                                     start_list=batch,
                                     fanout_vals=[10],
                                     with_replacement=False

332 ms ± 847

Expected Performance : I would expect the performance to be around 8-9x better. More details in internal thread.

[VibhuJawa]

Will be optimized by 50x 332 ms to 25 ms by https://github.com/rapidsai/cugraph/pull/2394 .

Of that 25 ms only 6.61 ms seems to spent in the pylibcugraph_uniform_neighbor_sample call so we have more scope there.

[github-actions[bot]]

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[kingmesal]

@seunghwak or @VibhuJawa
Can you confirm if this is complete? Does anything else need to occur to close this?

[seunghwak]

https://github.com/rapidsai/cugraph/pull/3269 might be related to this issue no other issue tracking uniform neighbor sampling performance.

[kingmesal]

@VibhuJawa can you confirm whether or not #3269 fixes your issue?

[VibhuJawa]

Lets close this issue. https://github.com/rapidsai/cugraph/pull/3269 fixed most things