[GitHub] [lucene-solr] jtibshirani edited a comment on issue #1314: LUCENE-9136: Coarse quantization that reuses existing formats.

[GitBox <gi...@apache.org>]

jtibshirani edited a comment on issue #1314: LUCENE-9136: Coarse quantization that reuses existing formats.
URL: https://github.com/apache/lucene-solr/pull/1314#issuecomment-594242054
 
 
   **Benchmarks**
   
   sift-128-euclidean: a dataset of 1 million SIFT descriptors with 128 dims.
   ```
   APPROACH                          RECALL     QPS
   LuceneExact()                     1.000        6.425
   LuceneCluster(n_probes=2)         0.536     1138.926
   LuceneCluster(n_probes=5)         0.749      574.186
   LuceneCluster(n_probes=10)        0.874      308.455
   LuceneCluster(n_probes=20)        0.951      116.871
   LuceneCluster(n_probes=50)        0.993       67.354
   LuceneCluster(n_probes=100)       0.999       34.651
   ```
   
   glove-100-angular: a dataset of ~1.2 million GloVe word vectors of 100 dims.
   ```
   APPROACH                          RECALL     QPS
   LuceneExact()                     1.000        6.722
   LuceneCluster(n_probes=5)         0.680      618.438
   LuceneCluster(n_probes=10)        0.766      335.956
   LuceneCluster(n_probes=20)        0.835      173.782
   LuceneCluster(n_probes=50)        0.905       72.747
   LuceneCluster(n_probes=100)       0.948       37.339
   ```
   
   These benchmarks were performed using the [ann-benchmarks repo](https://github.com/erikbern/ann-benchmarks). I hooked up the prototype to the benchmarking framework using py4j (e10d34c73dc391e4a105253f6181dfc0e9cb6705). Unfortunately py4j adds quite a bit of overhead (~3ms per search), so I had to measure that overhead and subtract it from the results. This is really not ideal, I will work on more robust benchmarks.
   

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