Join Magdalen Dobson Manohar, a brilliant scientist, on a journey into Approximate Nearest Neighbor Search. Explore topics like Parallel Computing, Lock Contention in HNSW, ParlayANN development, Filtered Vector Search, and exciting future directions in Vector Search technology.
ParlayANN explores parallel indexing without locking, offering a unique approach in vector database technology.
Prefix doubling enables edge insertion in graph-based algorithms, maintaining performance while handling large-scale computations.
Filtered search in ANN algorithms uses binary predicates for efficient pruning, optimizing distance calculations for enhanced search processes.
Deep dives
Explanation of Graph-Based Algorithms for Approximate Nearest Neighbor Search
Graph-based algorithms for approximate nearest neighbor search involve methods like disk ANN and HSNW that focus on building graph structures efficiently. These algorithms address challenges such as data races when adding edges to existing vertices. The process includes determining out neighbors and then adding reverse edges, ensuring degrees are bound within the graph.
Parallelization Techniques and Prefix Doubling Strategy
To parallelize the insertion of edges without locks, a prefix doubling strategy is employed. This technique involves batch ordering the inserts by incrementing batch sizes gradually. Prefix doubling ensures that the batch size remains manageable relative to the size of the graph, preventing performance degradation due to excessive connections.
Experimentation and Scaling Challenges in Billion-Scale Data Sets
Experimental goals involve testing algorithms at a billion-scale level, particularly with large datasets like SIFT vectors and YFCC. Challenges include addressing scaling issues, especially when transitioning from smaller datasets to billion-scale computations. The competitions like BigANN have been instrumental in advancing research by providing access to large-scale datasets for evaluation.
Approach to Filtered Search in Nearest Neighbor Algorithms
The podcast delves into a novel approach for filtered search in nearest neighbor algorithms, particularly focusing on vectors that meet specific Boolean predicates. By using the example of shopping on Amazon for shoes with multiple criteria like color, size, and heel height, the speaker explains the concept of applying binary predicates to prune search spaces efficiently. The development of the IVF squared algorithm tackles the challenges of filtered search by utilizing partitioning based on filters to optimize distance calculations and search processes, addressing significant issues in approximate nearest neighbor science.
Dynamic Indexing and Adaptive Quantization in Nearest Neighbor Algorithms
The discussion extends to dynamic graph-based algorithms in approximate nearest neighbor search that can update without locks, presenting a cutting-edge exploration in the field. The focus shifts towards the adaptation of quantization methods in evolving datasets, highlighting the importance of real-time adjustments to maintain optimal search accuracy. Additional considerations involve parallelizing k-means for quantization updates and leveraging GPU computing for accelerated quantization processes, reflecting a forward-looking approach to enhancing efficiency and scalability in nearest neighbor algorithms.
As you are graduating from ideas to engineering, one of the key concepts to be aware of is Parallel Computing and Concurrency. I am SUPER excited to share our 94th Weaviate podcast with Magdalen Dobson Manohar! Magdalen is one of the most impressive scientists I have ever met, having completed her undergraduate studies at MIT before joining Carnegie Mellon University to study Approximate Nearest Neighbor Search and develop ParlayANN. ParlayANN is one of the most enlightening works I have come across that studies how to build ANN indexes in parallel without the use of locking.
In my opinion, this is the most insightful podcast we have ever produced into Vector Search, the core technology behind Vector Databases. The podcast begins with Magdalen’s journey into ANN science, the issue of Lock Contention in HNSW, further detailing HNSW vs. DiskANN vs. HCNNG and pyNNDescent, ParlayIVF, how Parallel Index Construction is achieved, conclusions from experimentation, Filtered Vector Search, Out of Distribution Vector Search, and exciting directions for the future!
I also want to give a huge thanks to Etienne Dilocker, John Trengrove, Abdel Rodriguez, Asdine El Hrychy, and Zain Hasan. There is no way I would be able to keep up with conversations like this without their leadership and collaboration.
I hope you find the podcast interesting and useful!
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