Thanks Trevor, this is helpful. It also reminded me to reply here too on the off-heap scoring.
Scoring off-heap, and thus avoiding a copy, gains approx 2x in the vector comparisons - this is quite substantial and maybe align with what Trevor sees? However, because of a Hotspot bug, using MemorySegment is not yet an option for scoring float32’s off-heap [1]. We’ll get the Hotspot bug fixed, but in the mean time to help evaluate the potential gain I just wrote native float32 vector comparison operations to see how they perform in Elasticsearch [2]. -Chris. [1] https://mail.openjdk.org/pipermail/panama-dev/2025-June/021070.html [2] https://github.com/elastic/elasticsearch/pull/130541 > On 7 Jul 2025, at 21:01, Trevor McCulloch > <trevor.mccull...@mongodb.com.INVALID> wrote: > > For comparison I put together a codec that is a copy of Lucene99 but with > most of KnnVectorsReader.search() implemented in native code and called via > FFM as a way of examining overhead from the JVM. I didn't have the same data > set, but on 1M 1536d float vectors with default lucene99 settings it was > about 10% faster in native code -- not nothing, but not very much considering > the additional complexity of using native code. I was able to avoid copying > data from the mmap backing store in order to perform distance comparisons > which was probably a significant chunk of the win. The vast majority of CPU > time (~80%) is spent doing vector comparison and something like 95-96% of > that CPU time is spent scoring in L0. Decoding edges from the graph is ~1.5%. > I think so long as vector scoring code is competitive and Lucene is doing a > similar number of comparisons the margin should be pretty close. > > I looked through their open source implementation and did not see anything > that led me to believe that their HNSW implementation is substantially > different in an algorithmic sense. They did have some logic around choosing > different representations of the visited set depending on the expected number > of nodes to visit (choosing between ~FixedBitSet and a hash set). > > On Mon, Jun 23, 2025 at 6:10 AM Benjamin Trent <ben.w.tr...@gmail.com> wrote: > To my knowledge, FAISS isn't utilizing hand-rolled SIMD calculations. Do we > know if it was compiled with `--ffast-math`? > > Vespa does utilize SIMD optimizations for vector comparisons. > > Some more ways I think Lucene is slower (though, I am not sure the 2x is > fully explained): > > - Reading floats onto heap float[] instead of accessing Memory Segments > directly when scoring > - We store the graph in a unique way that requires a decoding step when > exploring a new candidate, reading in vints and doing a binary search. I > think all other hnsw impls do flat arrays of int/long values. > - We always use SparseBitSet, which for smaller indices <1M can have a > noticeable impact on performance. I have seen this in my own benchmarking > (switching to fixedbitset measurably improved query times on smaller data > sets) > > Both of these are fairly "cheap". Which might explain the FAISS 10% > difference. However, I am not sure they fully explain the 2x difference with > vespa. > > On Thu, Jun 19, 2025 at 3:37 PM Adrien Grand <jpou...@gmail.com> wrote: > Thanks Mike, this is useful information. Then I'll try to reproduce this > benchmark to better understand what is happening. > > On Thu, Jun 19, 2025 at 8:16 PM Michael Sokolov <msoko...@gmail.com> wrote: > We've recently been comparing Lucene's HNSW w/FAISS' and there is not > a 2x difference there. FAISS does seem to be around 10-15% faster I > think? The 2x difference is roughly what I was seeing in comparisons > w/hnswlib prior to the dot-product improvements we made in Lucene. > > On Thu, Jun 19, 2025 at 2:12 PM Adrien Grand <jpou...@gmail.com> wrote: > > > > Chris, > > > > FWIW I was looking at luceneknn > > (https://github.com/erikbern/ann-benchmarks/blob/f402b2cc17b980d7cd45241ab5a7a4cc0f965e55/ann_benchmarks/algorithms/luceneknn/Dockerfile#L15) > > which is on 9.7, though I don't know if it enabled the incubating vector > > API at runtime? > > > > I hope that mentioning ANN benchmarks did not add noise to this thread, I > > was mostly looking at whether I could find another benchmark that suggests > > that Lucene is significantly slower in similar conditions. Does it align > > with other people's experience that Lucene is 2x slower or more compared > > with other good HNSW implementations? > > > > Adrien > > > > Le jeu. 19 juin 2025, 18:44, Chris Hegarty > > <christopher.hega...@elastic.co.invalid> a écrit : > >> > >> Hi Adrien, > >> > >> > Even though it uses Elasticsearch to run the benchmark, it really > >> > benchmarks Lucene functionality, > >> > >> Agreed. > >> > >> > This seems consistent with results from > >> > https://ann-benchmarks.com/index.html though I don't know if the cause > >> > of the performance difference is the same or not. > >> > >> On ann-benchmarks specifically. Unless I’m looking in the wrong place, > >> then they’re using Elasticsearch 8.7.0 [1], which predates our usage of > >> the Panama Vector API for vector search. We added support for that in > >> Lucene 9.7.0 -> Elasticsearch 8.9.0. So those benchmarks are wildly out > >> of date, no ? > >> > >> -Chris. > >> > >> [1] > >> https://github.com/erikbern/ann-benchmarks/blob/f402b2cc17b980d7cd45241ab5a7a4cc0f965e55/ann_benchmarks/algorithms/elasticsearch/Dockerfile#L2 > >> > >> > >> > On 19 Jun 2025, at 16:39, Adrien Grand <jpou...@gmail.com> wrote: > >> > > >> > Hello all, > >> > > >> > I have been looking at this benchmark against Vespa recently: > >> > https://blog.vespa.ai/elasticsearch-vs-vespa-performance-comparison/. > >> > (The report is behind an annoying email wall, but I'm copying relevant > >> > data below, so hopefully you don't need to download the report.) Even > >> > though it uses Elasticsearch to run the benchmark, it really benchmarks > >> > Lucene functionality, I don't believe that Elasticsearch does anything > >> > that meaningfully alters the results that you would get if you were to > >> > run Lucene directly. > >> > > >> > The benchmark seems designed to highlight the benefits of Vespa's > >> > realtime design, that's fair game I guess. But it also runs some queries > >> > in read-only scenarios when I was expecting Lucene to perform better. > >> > > >> > One thing that got me curious is that it reports about 2x worse latency > >> > and throughput for pure unfiltered vector search on a force-merged index > >> > (so single segment/graph). Does anybody know why Lucene's HNSW may > >> > perform slower than Vespa's HNSW? This seems consistent with results > >> > from https://ann-benchmarks.com/index.html though I don't know if the > >> > cause of the performance difference is the same or not. > >> > > >> > For reference, here are details that apply to both Lucene and Vespa's > >> > vector search: > >> > - HNSW, > >> > - float32 vectors, no quantization, > >> > - embeddings generated using Snowflake's Arctic-embed-xs model > >> > - 1M docs > >> > - 384 dimensions, > >> > - dot product, > >> > - m = 16, > >> > - max connections = 200, > >> > - search for top 10 hits, > >> > - no filter, > >> > - single client, so no search concurrency, > >> > - purple column is force-merged, so single segment/graph like Vespa. > >> > > >> > I never seriously looked at Lucene's vector search performance, so I'm > >> > very happy to be educated if I'm making naive assumptions! > >> > > >> > Somewhat related, is this the reason why I'm seeing many threads around > >> > bringing 3rd party implementations into Lucene, including ones that are > >> > very similar to Lucene on paper? To speed up vector search? > >> > > >> > -- > >> > Adrien > >> > <vespa-vs-es-screenshot.png> > >> > --------------------------------------------------------------------- > >> > To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org > >> > For additional commands, e-mail: dev-h...@lucene.apache.org > >> > >> > >> > >> --------------------------------------------------------------------- > >> To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org > >> For additional commands, e-mail: dev-h...@lucene.apache.org > >> > > --------------------------------------------------------------------- > To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org > For additional commands, e-mail: dev-h...@lucene.apache.org > > > > -- > Adrien --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org For additional commands, e-mail: dev-h...@lucene.apache.org
