Tagged articles

Approximate Nearest Neighbor

19 articles · Page 1 of 1

Jul 23, 2026 · Artificial Intelligence

HELMSMAN: Redefining Large-Scale Vector Retrieval on All‑Flash Servers (OSDI 2026)

HELMSMAN replaces DRAM‑heavy graph indexes with a clustering‑based, SSD‑first ANN system that uses a custom SPDK storage stack, adaptive LLSP pruning, and a GPU‑CPU construction pipeline, achieving 2‑16× throughput, up to 85% of in‑memory performance, and over 90% hardware cost reduction for billion‑scale search workloads.

Approximate Nearest NeighborClusteringCost Efficiency

0 likes · 13 min read

HELMSMAN: Redefining Large-Scale Vector Retrieval on All‑Flash Servers (OSDI 2026)

Machine Heart

Jul 22, 2026 · Artificial Intelligence

HELMSMAN Redefines Large-Scale Vector Retrieval with High-Performance ANNS

The HELMSMAN system, presented by the Xiaohongshu engine team at OSDI 2026, demonstrates a cost‑effective, high‑throughput, low‑latency approximate nearest neighbor search solution that migrates billions of vectors from DRAM to NVMe SSD arrays using a custom storage stack, clustering‑based indexing, learned pruning, and GPU‑accelerated distributed construction, achieving up to 16× throughput gains and over 90% hardware cost reduction.

Approximate Nearest NeighborClusteringGPU Acceleration

0 likes · 13 min read

HELMSMAN Redefines Large-Scale Vector Retrieval with High-Performance ANNS

AI2ML AI to Machine Learning

Feb 27, 2026 · Artificial Intelligence

Why No Single Algorithm Dominates Vector Search: A Deep Dive into Modern Vector DBs

The article surveys emerging vector databases, explains how various vector‑search algorithms such as FLAT, IVF, HNSW, DiskANN and ScaNN differ in accuracy, speed, memory use and build time, and provides practical guidance for choosing the right index based on data size, latency and resource constraints.

Approximate Nearest NeighborDiskANNHNSW

0 likes · 9 min read

Why No Single Algorithm Dominates Vector Search: A Deep Dive into Modern Vector DBs

Alibaba Cloud Big Data AI Platform

Nov 6, 2025 · Artificial Intelligence

How GPU‑Accelerated NN‑Descent Boosts Vector Search Speed by Up to 13×

This article explains how unstructured multimedia data is transformed into vectors for similarity search, introduces GPU parallelism and the NN‑Descent algorithm to replace traditional HNSW indexing in OpenSearch, and presents benchmark results showing up to a thirteen‑fold speed improvement while maintaining comparable recall.

Approximate Nearest NeighborGPU AccelerationNN-Descent

0 likes · 12 min read

How GPU‑Accelerated NN‑Descent Boosts Vector Search Speed by Up to 13×

AntTech

Sep 16, 2025 · Information Security

Cutting-Edge Privacy Tech Unveiled: Gibbon, Panther & PromeFuzz at ACM CCS 2025

At the ACM CCS 2025 live paper showcase, three groundbreaking studies—Gibbon’s fast secure two‑party GBDT training, Panther’s efficient private approximate nearest‑neighbor search on a single server, and PromeFuzz’s knowledge‑driven LLM approach to fuzzing harness generation—are presented, highlighting significant performance and security advances.

Approximate Nearest NeighborFuzzingLLM

0 likes · 8 min read

Cutting-Edge Privacy Tech Unveiled: Gibbon, Panther & PromeFuzz at ACM CCS 2025

StarRocks

Feb 11, 2025 · Databases

How StarRocks Supercharges Vector Search: 7× Faster Queries and 1/3 Cost

This article explains the principles and practical implementation of vector retrieval in StarRocks, covering approximate nearest‑neighbor algorithms, index design, query planning, performance optimizations, real‑world case studies, and future challenges, showing how query latency dropped from 15 seconds to 2 seconds while cutting costs to a third.

ANNApproximate Nearest NeighborHNSW

0 likes · 25 min read

How StarRocks Supercharges Vector Search: 7× Faster Queries and 1/3 Cost

Alibaba Cloud Big Data AI Platform

Dec 18, 2024 · Artificial Intelligence

Can GPU Graph Algorithms Boost Vector Search Performance by 10×?

This article explains how OpenSearch's GPU‑accelerated vector search leverages parallel graph algorithms to achieve up to tenfold speed improvements over CPU solutions, detailing ANNS techniques, performance benchmarks, and practical GPU specifications for high‑QPS AI applications.

Approximate Nearest NeighborGPU AccelerationOpenSearch

0 likes · 11 min read

Can GPU Graph Algorithms Boost Vector Search Performance by 10×?

Xiaohongshu Tech REDtech

Nov 7, 2024 · Artificial Intelligence

RTAMS-GANNS: A Real-Time Adaptive Multi-Stream GPU System for Online Approximate Nearest Neighbor Search

RTAMS‑GANNS, the award‑winning real‑time adaptive multi‑stream GPU system for online approximate nearest neighbor search, eliminates costly memory allocations and serial execution by using a dynamic memory‑block insertion algorithm and separate CUDA streams, cutting latency by 40‑80% and reliably serving over 100 million daily users in production.

Approximate Nearest NeighborGPUHigh QPS

0 likes · 19 min read

RTAMS-GANNS: A Real-Time Adaptive Multi-Stream GPU System for Online Approximate Nearest Neighbor Search

Ops Development & AI Practice

Mar 13, 2024 · Artificial Intelligence

How Vector Retrieval Powers AI Model Training and Real-World Applications

Vector retrieval, based on converting data into high‑dimensional vectors and measuring similarity, enables fast, accurate search across massive datasets, supporting AI tasks such as search engines, recommendation, NLP, and computer vision, and plays a crucial role in large‑model training for data selection, anomaly detection, and model optimization.

AI trainingApproximate Nearest NeighborRecommendation Systems

0 likes · 6 min read

How Vector Retrieval Powers AI Model Training and Real-World Applications

Baidu Tech Salon

Sep 25, 2023 · Artificial Intelligence

Approximate Nearest Neighbor Retrieval and Baidu's Open‑Source Puck Framework – Event Overview

Baidu’s upcoming technical salon series on search will showcase its open‑source ANN retrieval framework Puck—demonstrating superior performance on massive benchmarks—and feature nightly live sessions covering product management, GPU optimization, intelligent Q&A, model architecture, and image generation, with registration via QR‑code.

AIApproximate Nearest NeighborBaidu

0 likes · 2 min read

Approximate Nearest Neighbor Retrieval and Baidu's Open‑Source Puck Framework – Event Overview

Baidu Geek Talk

Aug 9, 2023 · Industry Insights

Why Vector Retrieval Is the Backbone of Modern LLM Applications

The article explains how vectors represent data in high‑dimensional space, describes the embedding process, outlines the evolution and challenges of vector search, compares exact and approximate algorithms such as IVF, product quantization and HNSW, and details Baidu’s cloud‑native engineering solutions for scalable, filtered vector retrieval.

AIApproximate Nearest NeighborCloud Native

0 likes · 14 min read

Why Vector Retrieval Is the Backbone of Modern LLM Applications

DataFunSummit

Aug 3, 2023 · Artificial Intelligence

Integrating Vector Databases with Large Language Models for Enterprise AI Applications

The article explains how combining vector databases with large language models can help governments and enterprises leverage massive private data for AI, covering semantic search, approximate nearest neighbor techniques, alignment challenges across modalities, and future directions for fine‑grained data integration.

AIApproximate Nearest NeighborVector Database

0 likes · 7 min read

Integrating Vector Databases with Large Language Models for Enterprise AI Applications

Kuaishou Tech

Apr 24, 2023 · Artificial Intelligence

Divide‑and‑Conquer Embedding‑Based Retrieval with Prompt‑Based Multi‑Task Learning for Large‑Scale Recommendation

This paper identifies the trade‑off between simple and hard negatives in embedding‑based retrieval for recommendation, proposes a clustering‑based divide‑and‑conquer framework combined with prompt‑driven multi‑task learning to improve relevance, diversity, and fairness, and validates the approach through offline metrics, online A/B tests, and comparative experiments.

Approximate Nearest NeighborDivide and ConquerEmbedding Retrieval

0 likes · 9 min read

Divide‑and‑Conquer Embedding‑Based Retrieval with Prompt‑Based Multi‑Task Learning for Large‑Scale Recommendation

Laiye Technology Team

Apr 29, 2022 · Artificial Intelligence

Using Faiss for Efficient Vector Similarity Search: Installation, Index Construction, and Performance Optimization

This tutorial explains what Faiss is, how to install it, construct various indexes such as IndexFlatL2, IndexIVFFlat, and IndexIVFPQ, and demonstrates code examples for building and querying vector similarity search pipelines while discussing speed‑accuracy trade‑offs.

AIApproximate Nearest NeighborFAISS

0 likes · 11 min read

Using Faiss for Efficient Vector Similarity Search: Installation, Index Construction, and Performance Optimization

Baidu Geek Talk

Feb 14, 2022 · Artificial Intelligence

How Baidu’s PUCK Dominated the First BigANN Vector Search Competition

The inaugural BigANN competition, organized by NeurIPS, showcased large‑scale ANN research, and Baidu's self‑developed PUCK algorithm secured top scores across all four tracks by leveraging multi‑layer quantization, two‑level inverted indexing, and extensive system‑level optimizations.

ANNApproximate Nearest NeighborBigANN

0 likes · 8 min read

How Baidu’s PUCK Dominated the First BigANN Vector Search Competition

IEG Growth Platform Technology Team

Jan 17, 2022 · Artificial Intelligence

Introduction to Vector Retrieval, Distance Metrics, and Fundamental Algorithms

This article introduces the concept of vector retrieval, outlines its diverse application scenarios, explains common distance metrics for both floating‑point and binary vectors, and surveys fundamental approximate nearest‑neighbor algorithms including tree‑based, graph‑based, quantization, and hashing methods.

Approximate Nearest NeighborHNSWKD-Tree

0 likes · 22 min read

Introduction to Vector Retrieval, Distance Metrics, and Fundamental Algorithms

DataFunSummit

Jan 10, 2022 · Artificial Intelligence

Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms

This article explains how deep learning transforms raw physical‑world data into dense vectors, defines the significance of vector retrieval, surveys common use cases such as image, video, and text search, discusses challenges in representation learning, and reviews high‑performance approximate nearest‑neighbor algorithms and practical deployments.

AI ApplicationsApproximate Nearest Neighbordeep learning

0 likes · 21 min read

Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms

Laiye Technology Team

Jan 7, 2022 · Artificial Intelligence

Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms

This article explains how deep learning transforms unstructured data into dense vectors, defines vector retrieval, outlines its many use cases such as product, video, and text search, discusses challenges in learning effective embeddings, and reviews high‑performance algorithms like LSH, neighbor graphs, and product quantization.

AI ApplicationsApproximate Nearest NeighborHNSW

0 likes · 21 min read

iQIYI Technical Product Team

Feb 21, 2020 · Artificial Intelligence

Top-1 Solution for the 2019 CCF Big Data & Computing Intelligence Competition: Video Copyright Detection

The Hengyang Data team won the 2019 CCF Big Data & Computing Intelligence video‑copyright detection contest by extracting VGG16‑based image features with Gaussian‑R‑MAC weighting, using a graph‑based NSG nearest‑neighbor search and a frame‑matching algorithm to locate infringing segments within three‑second precision, even under severe cropping and other transformations.

Approximate Nearest NeighborCCF competitionVGG16

0 likes · 9 min read

Top-1 Solution for the 2019 CCF Big Data & Computing Intelligence Competition: Video Copyright Detection