GTS
GTS is a GPU-based tree index for fast similarity search over high-dimensional vector data, providing an efficient ANN index structure that can be integrated into or used to build high-performance vector database systems.
About this tool
GTS
Type: Research paper / GPU-based index structure
Category: Research Papers & Surveys
Tags: similarity-search, ANN, gpu-acceleration
GTS is a GPU-based tree index for fast similarity search in general metric spaces, aimed at high-dimensional vector and other complex data types. It is designed to support high-throughput, concurrent similarity queries where only a distance function is available, and to be integrated into high-performance (approximate) nearest-neighbor and vector database systems.
Key Concepts
- Domain: Similarity search in general metric spaces (not limited to Euclidean/coordinate data).
- Goal: Accelerate similarity search where distance computation is expensive and coordinate-based acceleration is not available.
- Hardware focus: GPU-based parallel index and query processing.
- Use cases: Multimedia retrieval, decision-making systems, visualization recommendation, bioinformatics and omics data, text and sequence data, social media / online commercial databases.
Features
Index Structure
- GPU-based tree index (GTS):
- Designed specifically for similarity search in general metric spaces.
- Operates only on a distance metric; does not require explicit coordinates.
- Pivot-based tree structure:
- Uses pivots (reference objects) to organize data.
- Enables effective pruning of candidate objects during search.
- List-table organization:
- Employs list tables to structure data and index information for GPU-friendly access patterns.
- Optimizes memory coalescing and parallel computation on GPUs.
Query Processing & Parallelism
- Concurrent similarity search:
- Supports many similarity queries executing in parallel on GPU.
- Tailored to meet high-throughput data management requirements.
- Two-stage search method:
- Batch processing stage: Groups multiple queries to exploit GPU parallelism and amortize computation.
- Sequential (or follow-up) stage: Handles remaining work to better utilize limited GPU memory and handle large query loads.
- Combined strategy balances throughput and memory constraints.
- General metric support:
- Works with arbitrary distance functions (e.g., cosine distance, edit distance, domain-specific metrics) as long as metric properties hold.
Update & Maintenance
- Streaming data updates:
- Supports incremental updates to the index as new data arrives in a streaming fashion.
- Batch data updates:
- Provides efficient mechanisms for bulk updates or reorganization when large sets of data change.
- Cost-effective updates for dynamic datasets:
- Targets use cases like social media and online commercial databases where data changes frequently.
Performance Modeling
- Cost model for search performance:
- The paper presents a cost model to estimate or analyze the performance of similarity search using GTS.
- Helps guide parameter tuning and system design/integration.
Experimental Results (as reported)
- Datasets: Evaluated on five real-world datasets.
- Efficiency vs CPU baselines:
- Reported speedups up to two orders of magnitude over existing CPU-based methods.
- Efficiency vs GPU baselines:
- Reported up to 20× efficiency improvement over state-of-the-art GPU-based similarity search methods.
Applications
- High-dimensional vector search and ANN index within vector databases.
- General-purpose similarity search in metric spaces for:
- Multimedia and image retrieval.
- Text and document similarity (e.g., cosine distance).
- Biological sequence comparison (e.g., edit distance for DNA strings).
- Heterogeneous cancer omics and other scientific data.
- Dynamic, large-scale social and commercial datasets needing frequent updates.
Pricing
- Not applicable (research paper / algorithm; no pricing information provided).
Source
- Paper: "GTS" – GPU-based tree index for similarity search in metric spaces
- URL: https://arxiv.org/html/2404.00966v1
Loading more......
Information
Categories
Tags
Similar Products
6 result(s)Cagra provides highly parallel graph construction and approximate nearest neighbor search for GPUs, supporting large-scale vector database operations and efficient similarity search.
RTNN is a research prototype system and codebase that accelerates high-dimensional nearest neighbor search using hardware ray tracing units on modern GPUs. It targets vector similarity search workloads common in AI applications, exploring ray-tracing hardware as an alternative acceleration path to traditional CPU- or CUDA-based ANN indexes.
AiSAQ is an all-in-storage approximate nearest neighbor search system that uses product quantization to enable DRAM-free vector similarity search, serving as a specialized vector search/indexing approach for large-scale information retrieval.
This work by Jingfan Meng is a comprehensive research thesis on efficient locality-sensitive hashing (LSH), covering algorithmic solutions, core primitives, and applications for approximate nearest neighbor search. It is relevant to vector databases because LSH-based indexing is a foundational technique for scalable similarity search over high-dimensional vectors, informing the design of vector indexes, retrieval engines, and similarity search modules in modern vector database systems.
BANG is a billion-scale approximate nearest neighbor search system optimized for single GPU execution, enabling high-performance vector search in vector database environments at massive scale.
Ruby gem for approximate nearest neighbor search that can integrate with pgvector and other backends to power vector similarity search in Ruby applications.