BatANN delivers near-linear throughput scaling for distributed disk-based approximate nearest neighbor search on a single global graph, with 3.5-5.59x gains over scatter-gather baselines on 1B-point datasets at 0.95 recall.
AiSAQ: All-in-storage ANNS with product quanti- zation for DRAM-free information retrieval
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HRNN combines a navigation graph, ranked KNN graph, and reverse-neighbor lists with proxy-based candidate generation and materialized kNN-radii to achieve up to 10x higher throughput for approximate RkNN on datasets up to 10M vectors.
KScaNN delivers up to 1.63x speedup on Kunpeng ARM over the best x86 ANNS solutions via hybrid intra-cluster search, improved PQ residuals, an ML adaptive module, and ARM-optimized SIMD kernels.
ScaleGANN accelerates graph-based ANN index construction up to 9x faster and 6x cheaper than DiskANN by using divide-and-merge on distributed low-cost spot GPUs with optimized partitioning and a cost-aware scheduler.
A survey that categorizes RAG methods for LLMs into four retrieval-centric stages, reviews their evolution and evaluation, and outlines challenges and future directions.
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Passing the Baton: High Throughput Distributed Disk-Based Vector Search with BatANN
BatANN delivers near-linear throughput scaling for distributed disk-based approximate nearest neighbor search on a single global graph, with 3.5-5.59x gains over scatter-gather baselines on 1B-point datasets at 0.95 recall.
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HRNN: A Hybrid Graph Index for Approximate Reverse k-Nearest Neighbor Search on High-Dimensional Vectors
HRNN combines a navigation graph, ranked KNN graph, and reverse-neighbor lists with proxy-based candidate generation and materialized kNN-radii to achieve up to 10x higher throughput for approximate RkNN on datasets up to 10M vectors.
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KScaNN: Scalable Approximate Nearest Neighbor Search on Kunpeng
KScaNN delivers up to 1.63x speedup on Kunpeng ARM over the best x86 ANNS solutions via hybrid intra-cluster search, improved PQ residuals, an ML adaptive module, and ARM-optimized SIMD kernels.
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ScaleGANN: Accelerate Large-Scale ANN Indexing by Cost-effective Cloud GPUs
ScaleGANN accelerates graph-based ANN index construction up to 9x faster and 6x cheaper than DiskANN by using divide-and-merge on distributed low-cost spot GPUs with optimized partitioning and a cost-aware scheduler.
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A Survey on Retrieval-Augmented Text Generation for Large Language Models
A survey that categorizes RAG methods for LLMs into four retrieval-centric stages, reviews their evolution and evaluation, and outlines challenges and future directions.