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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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.
citing papers explorer
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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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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.