Julie Tibshirani created LUCENE-10527:
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Summary: Use bigger maxConn for last layer in HNSW
Key: LUCENE-10527
URL: https://issues.apache.org/jira/browse/LUCENE-10527
Project: Lucene - Core
Issue Type: Task
Reporter: Julie Tibshirani
Attachments: hnsw_plot.png, image-2022-04-20-14-53-58-484.png
Recently I was rereading the HNSW paper
([https://arxiv.org/pdf/1603.09320.pdf)] and noticed that they suggest using a
different maxConn for the upper layers vs. the bottom one (which contains the
full neighborhood graph). Specifically, they suggest using maxConn=M for upper
layers and maxConn=2*M for the bottom. This differs from what we do, which is
to use maxConn=M for all layers.
I tried updating our logic using a hacky patch, and noticed an improvement in
latency for higher QPS values (which is consistent with the paper's
observation):
*Results on glove-100-angular*
Parameters: M=32, efConstruction=100
!image-2022-04-20-14-53-58-484.png!
As we'd expect, indexing becomes a bit slower:
{code:java}
Baseline: Indexed 1183514 documents in 733s
Candidate: Indexed 1183514 documents in 948s{code}
When we benchmarked Lucene HNSW against hnswlib in LUCENE-9937, we noticed a
big difference in recall for the same settings of M and efConstruction. (Even
adding graph layers in LUCENE-10054 didn't really affect recall.) With this
change, the recall is now very similar:
*Results on glove-100-angular*
Parameters: M=32, efConstruction=100
{code:java}
num_cands Approach Recall
QPS
10 luceneknn dim=100 {'M': 32, 'efConstruction': 100} 0.563
4410.499
50 luceneknn dim=100 {'M': 32, 'efConstruction': 100} 0.798
1956.280
100 luceneknn dim=100 {'M': 32, 'efConstruction': 100} 0.862
1209.734
100 luceneknn dim=100 {'M': 32, 'efConstruction': 100} 0.958 341.428
800 luceneknn dim=100 {'M': 32, 'efConstruction': 100} 0.974
230.396
1000 luceneknn dim=100 {'M': 32, 'efConstruction': 100} 0.980
188.757
10 hnswlib ({'M': 32, 'efConstruction': 100}) 0.552
16745.433
50 hnswlib ({'M': 32, 'efConstruction': 100}) 0.794
5738.468
100 hnswlib ({'M': 32, 'efConstruction': 100}) 0.860
3336.386
500 hnswlib ({'M': 32, 'efConstruction': 100}) 0.956
832.982
800 hnswlib ({'M': 32, 'efConstruction': 100}) 0.973
541.097
1000 hnswlib ({'M': 32, 'efConstruction': 100}) 0.979
442.163
{code}
I think it'd be nice update to maxConn so that we faithfully implement the
paper's algorithm. This is probably least surprising for users, and I don't see
a strong reason to take a different approach from the paper? Let me know what
you think!
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