kaivalnp commented on PR #14863:
URL: https://github.com/apache/lucene/pull/14863#issuecomment-3289707525
> I'll try to run a test with 8-bit quantization too, I realized this PR
will implicitly support it :)
Here are the benchmarks:
### `cosine`
`main`
```
recall latency(ms) netCPU avgCpuCount nDoc topK fanout maxConn
beamWidth quantized index(s) index_docs/s force_merge(s) num_segments
index_size(MB) vec_disk(MB) vec_RAM(MB) indexType
0.584 1.470 1.469 0.999 200000 100 50 32
200 8 bits 10.32 19372.34 14.67 1
747.53 733.185 147.247 HNSW
```
This PR
```
recall latency(ms) netCPU avgCpuCount nDoc topK fanout maxConn
beamWidth quantized index(s) index_docs/s force_merge(s) num_segments
index_size(MB) vec_disk(MB) vec_RAM(MB) indexType
0.583 1.316 1.314 0.998 200000 100 50 32
200 8 bits 10.54 18978.93 13.66 1
747.16 733.185 147.247 HNSW
```
### `dot_product`
`main`
```
recall latency(ms) netCPU avgCpuCount nDoc topK fanout maxConn
beamWidth quantized index(s) index_docs/s force_merge(s) num_segments
index_size(MB) vec_disk(MB) vec_RAM(MB) indexType
0.858 1.444 1.442 0.999 200000 100 50 32
200 8 bits 10.38 19267.82 14.96 1
747.12 733.185 147.247 HNSW
```
This PR
```
recall latency(ms) netCPU avgCpuCount nDoc topK fanout maxConn
beamWidth quantized index(s) index_docs/s force_merge(s) num_segments
index_size(MB) vec_disk(MB) vec_RAM(MB) indexType
0.858 1.315 1.314 0.999 200000 100 50 32
200 8 bits 11.21 17836.44 14.28 1
747.16 733.185 147.247 HNSW
```
### `euclidean`
`main`
```
recall latency(ms) netCPU avgCpuCount nDoc topK fanout maxConn
beamWidth quantized index(s) index_docs/s force_merge(s) num_segments
index_size(MB) vec_disk(MB) vec_RAM(MB) indexType
0.933 1.360 1.359 0.999 200000 100 50 32
200 8 bits 14.75 13562.08 34.43 1
740.41 733.185 147.247 HNSW
```
This PR
```
recall latency(ms) netCPU avgCpuCount nDoc topK fanout maxConn
beamWidth quantized index(s) index_docs/s force_merge(s) num_segments
index_size(MB) vec_disk(MB) vec_RAM(MB) indexType
0.935 1.288 1.286 0.998 200000 100 50 32
200 8 bits 10.04 19920.32 12.47 1
740.53 733.185 147.247 HNSW
```
### `mip`
`main`
```
recall latency(ms) netCPU avgCpuCount nDoc topK fanout maxConn
beamWidth quantized index(s) index_docs/s force_merge(s) num_segments
index_size(MB) vec_disk(MB) vec_RAM(MB) indexType
0.857 1.400 1.399 0.999 200000 100 50 32
200 8 bits 10.70 18686.35 14.36 1
747.11 733.185 147.247 HNSW
```
This PR
```
recall latency(ms) netCPU avgCpuCount nDoc topK fanout maxConn
beamWidth quantized index(s) index_docs/s force_merge(s) num_segments
index_size(MB) vec_disk(MB) vec_RAM(MB) indexType
0.858 1.358 1.357 0.999 200000 100 50 32
200 8 bits 10.47 19109.50 14.30 1
747.15 733.185 147.247 HNSW
```
Indexing and force-merge are non-trivially faster (30% and 60% respectively)
for `euclidean`, not sure if this is an outlier..
Search is slightly faster (3-10%) for all vector similarities
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