HeCun Vector Database
HetuVDB
Supporting the development of artificial intelligence in China
HetuVDB, developed by the Penglai Data Store, is a high-performance vector database designed to support vector retrieval and storage. Built to accelerate AI model training, it efficiently stores and retrieves vector data, enabling similarity search and complex queries. HetuVDB empowers enterprises across key use cases including large-scale AI model training acceleration, image recognition, natural language processing, recommendation systems, and question-answering platforms.
HetuVDB supports GPU acceleration and integrates with mainstream frameworks like PyTorch and TensorFlow. It leverages multi-core CPU optimization and flash storage capabilities to boost query speed and storage efficiency, ensuring high performance even when processing massive datasets.
HetuVDB ensures stable performance even as data grows by releasing memory resources efficiently and leveraging a proprietary high-performance vector indexing algorithm. With flexible APIs and comprehensive documentation, HetuVDB enables developers to get started quickly and deploy applications with ease. By accelerating AI model training and inference, HetuVDB fully harnesses the power of GPUs, CPUs, and SSDs to support efficient storage and similarity search for high-dimensional vector data. Widely used in artificial intelligence and machine learning, it delivers a secure, reliable, and high-performance domestic storage solution that advances China's AI industry.
Core Features
Vector data storage and retrieval engine designed for the AI era
self-reliant and controllable
Proprietary core technology, secure and controllable
Support vector search
Efficient AI Vector Similarity Search
Multi-core scalable
Fully utilize multi-core processor performance
Cross-platform compatible
Cross-platform deployment capability
GPU Support
GPU-accelerated computing power
Easy to integrate
Quickly integrate with existing systems
Highly Customizable
Adapts flexibly to diverse business scenarios
Industry Challenges
Technical Challenges and Strategies for Vector Databases
Storage requirements are too high
Vector databases must store massive amounts of high-dimensional data, demanding significant storage capacity.
Increased computational complexity
Computational complexity for high-dimensional data increases significantly with both sample size and dimensionality.
Balancing Precision and Efficiency
Processing vector data involves a trade-off between precision and efficiency. High-precision calculations typically require fast storage like SSDs, which can reduce access speed.
Developer-friendly
Vector database design should prioritize developer experience.
Real-time data storage
In many applications, vector databases must support real-time processing to return query results within milliseconds.
Index Management
Effective index management is key to vector database performance. As data grows, safely building, updating, and maintaining efficient indexes becomes increasingly critical.
Product Benefits
Core Competitiveness of HeCun Vector Database
Free memory resources
Break file system limits by using SSDs as cache and storage, freeing up memory for more compute tasks.
User-friendly
Easy to deploy and use, with extensive documentation and examples to help developers get started quickly and integrate effectively into existing systems.
GPU Support
Integrates with a vector database and GPU for efficient collaboration, leveraging CUDA cores for parallel processing to significantly boost vector data processing speed and computational efficiency.
Flash-optimized
HeCun Vector Database leverages the parallel advantages of flash memory to efficiently write data to disk in real time and immediately enable retrieval and operations, ensuring users access the latest data without delay.
Supports multimodal data
The Harmony Vector Database provides efficient storage and retrieval for unstructured data, including text, images, videos, and high-dimensional vectors.
Efficient Indexing
Efficient graph algorithms for fast, high-precision high-dimensional vector similarity search with low memory usage.
Product Solution
Empower diverse industry use cases and drive intelligent transformation.
AI Training
Video Recommendation System
Precise Semantic Similarity
Image Search
AI Training
By leveraging multi-core CPU processing and SSD parallel read/write capabilities, Hecun Vector Database significantly accelerates data writing and retrieval. During large-scale AI model training, massive datasets require frequent I/O operations and rapid data transfer. Hecun Database efficiently delivers this data to models for training, substantially reducing training time and costs. With optimized data paths and storage management, it maintains high performance even when handling large datasets, meeting the stringent speed requirements of deep learning and machine learning workloads.
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