Breaking the Storage-Compute Bottleneck in Billion-Scale ANNS
A 2025 research paper presenting a GPU-driven asynchronous I/O framework for billion-scale approximate nearest neighbor search. The system addresses the fundamental bottleneck of data movement between storage and compute in large-scale vector search.
About this tool
Overview
Published in July 2025 (arXiv:2507.10070), this paper presents a GPU-driven asynchronous I/O framework that breaks through the storage-compute bottleneck limiting billion-scale vector search systems.
The Bottleneck Problem
For billion-scale datasets exceeding GPU memory:
- Data must be loaded from storage (SSD) during search
- I/O bandwidth becomes the limiting factor
- GPU compute sits idle waiting for data
- Traditional synchronous I/O wastes resources
Asynchronous I/O Framework
The key innovation is overlapping I/O and computation:
- Prefetch Next Data: While GPU processes current batch, asynchronously load next batch
- Pipeline Execution: Continuous stream of data to GPU
- Minimize Idle Time: Keep GPU utilized while I/O happens in background
- Adaptive Scheduling: Adjust prefetch based on query patterns
GPU-Driven Design
Unlike CPU-managed I/O:
- GPU directly controls data movement
- Reduces CPU bottleneck
- Lower latency for I/O decisions
- Better alignment with GPU compute patterns
Performance Benefits
- Throughput: Maximizes GPU utilization by eliminating I/O wait time
- Latency: Reduces query latency through intelligent prefetching
- Scalability: Enables billion-scale search with single GPU
- Efficiency: Better resource utilization vs. synchronous approaches
Technical Contributions
Smart Prefetching
Algorithms to predict which data will be needed next based on graph traversal patterns
Overlap Optimization
Methods to maximize overlap between I/O and computation phases
Memory Management
Strategies for efficiently managing limited GPU memory as a cache for SSD data
Use Cases
- Billion-scale semantic search on single GPU
- Cost-effective large-scale deployments
- Systems where dataset >> GPU memory
- Applications requiring both scale and speed
Significance
As vector datasets grow, the storage-compute interface becomes critical. This research provides practical techniques for efficiently bridging SSD storage and GPU computation—essential for making billion-scale search economical.
Availability
ArXiv preprint arXiv:2507.10070 (2025) with detailed algorithms and experimental results.
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