Presentation
TierANNS: Scalable Graph-Based ANNS with CXL-Enabled Tiered Data Placement
DescriptionPerforming Approximate Nearest Neighbor Search (ANNS) on large-scale vector datasets is essential in the context of AI applications. Graph-based ANNS demonstrates superior performance and accuracy, positioning itself as a leading approach within the ANNS landscape. However, its inherent dependency on random data access mandates a memory-centric deployment strategy, which in turn presents significant scalability challenges.
Recent advancements in Compute Express Link (CXL) technologies, known for their high-bandwidth memory extension capabilities, offer a critical opportunity to enhance the scalability of graph-based ANNS. Nonetheless, the implications of the CXL-extended memory architecture when applied to graph-based ANNS remain largely unexplored.
In this paper, we present a CXL-Oriented Graph-based ANNS (COGA) system to achieve scalable, high-speed ANNS for extensive datasets. Our key observation is the search pipeline can be enhanced through a CXL-tailored priority queue that maximizes CXL bandwidth utilization while bridging the latency gap. Furthermore, we propose a tiered data layout and placement strategy that leverages queue hints to facilitate speculative access to index data.
Recent advancements in Compute Express Link (CXL) technologies, known for their high-bandwidth memory extension capabilities, offer a critical opportunity to enhance the scalability of graph-based ANNS. Nonetheless, the implications of the CXL-extended memory architecture when applied to graph-based ANNS remain largely unexplored.
In this paper, we present a CXL-Oriented Graph-based ANNS (COGA) system to achieve scalable, high-speed ANNS for extensive datasets. Our key observation is the search pipeline can be enhanced through a CXL-tailored priority queue that maximizes CXL bandwidth utilization while bridging the latency gap. Furthermore, we propose a tiered data layout and placement strategy that leverages queue hints to facilitate speculative access to index data.
Event Type
Research Manuscript
TimeMonday, July 2711:36am - 11:50am PDT
LocationMtg Room 202C
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