Discover how modern Java features, including vectorization and FFI/madvise, are speeding up Lucene's performance.

Foreign memory

Foreign function

Vectorization

Conclusion

Lucene & Java: How vectorization & FFI/madvise speed up Lucene

Making Lucene faster with vectorization and FFI/madvise

Learn how to use fused multiply-add (FMA) within vector similarity computations in Lucene and discover how FMA can improve performance.

What is fused multiply-add (FMA)

FMA in Lucene: Under the hood

Performance improvements with FMA

Wrapping up

Fused multiply-add (FMA) is a single operation that performs both the multiply and add operations in one - the multiplication and addition are said to be “fused” together. FMA is typically faster than a separate multiplication and addition and produces more accurate results.

What is fused multiply-add (FMA)?

Understanding fused multiply add (FMA) in Lucene

Understanding fused multiply-add (FMA) within vector similarity computations in Lucene

Learn how to optimize vector distance computations using the Foreign Function & Memory (FFM) API to achieve faster performance.

Current state of play: The Panama Vector API

Introducing the Foreign Function & Memory (FFM) API

FFM usage example

Going foreign

Back in Java-land

Performance improvements with FFM API

Summary

Optimizing vector distance computations with the FFM API

Optimizing vector distance computations with the Foreign Function & Memory (FFM) API

Author

Chris Hegarty

Articles

Making Lucene faster with vectorization and FFI/madvise

Understanding fused multiply-add (FMA) within vector similarity computations in Lucene

Optimizing vector distance computations with the Foreign Function & Memory (FFM) API