Onyx inverts ANN-ORAM optimization priorities with a compact pruning representation and locality-aware shallow tree to deliver 1.7-9.9x lower cost and 2.3-12.3x lower latency for disk-oblivious ANN search.
Bang: Billion-scale approximate nearest neighbor search using a single gpu,
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LSM-VEC integrates hierarchical graphs with LSM-tree levels for out-of-place dynamic updates, sampling-based search, and connectivity-aware reordering, outperforming prior disk-based ANN systems on billion-scale data with higher recall, lower latency, and over 66% memory reduction.
Co-design of 14.5x compacted index, asynchronous scheduler, and multiplication-free kernel for PIM-based graph ANNS delivers up to 20x CPU and 17.1x GPU throughput on billion-scale benchmarks.
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Onyx: Cost-Efficient Disk-Oblivious ANN Search
Onyx inverts ANN-ORAM optimization priorities with a compact pruning representation and locality-aware shallow tree to deliver 1.7-9.9x lower cost and 2.3-12.3x lower latency for disk-oblivious ANN search.
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LSM-VEC: A Large-Scale Disk-Based System for Dynamic Vector Search
LSM-VEC integrates hierarchical graphs with LSM-tree levels for out-of-place dynamic updates, sampling-based search, and connectivity-aware reordering, outperforming prior disk-based ANN systems on billion-scale data with higher recall, lower latency, and over 66% memory reduction.
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Co-Designing Graph-based Approximate Nearest Neighbor Search at Billion Scale for Processing-in-Memory
Co-design of 14.5x compacted index, asynchronous scheduler, and multiplication-free kernel for PIM-based graph ANNS delivers up to 20x CPU and 17.1x GPU throughput on billion-scale benchmarks.