Unlocking GPU Powers for Vector Search: OpenSearch's New Frontier

Unlocking GPU Powers for Vector Search: OpenSearch’s New Frontier - Navneet Verma & Jay Deng, Amazon Web Services As vector datasets explode to billions of entries, traditional CPU-based indexing methods are hitting a wall. Talk explores revolutionary approach to supercharge OpenSearch Vector Engine using GPU-accelerated vector index building. We’ll dive into three key features that demonstrate the power of GPU acceleration: 1. Lightning-fast index building: Witness how NVIDIA’s CAGRA algorithm enables 10-30x faster index build times compared to CPU solutions. 2. Seamless CPU-GPU integration: Dive deep into innovative architecture that introduces a GPU-powered remote fleet for vector index building, integrating smoothly with the existing CPU-based OpenSearch vector engine. Learn how this decoupled design allows for independent evolution of components. 3. Scalability and multi-tenancy: Discover how our approach enables efficient handling of massive datasets across multiple clusters, opening new possibilities for large-scale AI applications. Users’ll gain insights into: * The current challenges in vector search and why GPU acceleration is a game-changer * Improved error handling through intermediate data storage layers * The implications of this technology for the future of vector databases