Research talk: SPTAG++: Fast hundreds of billions-scale vector search with millisecond response time
Current state-of-the-art vector approximate nearest neighbor search (ANNS) libraries mainly focus on how to do fast high-recall search in memory. However, extremely large-scale vector search scenarios present certain challenges. For example, hundreds of billions of vectors coupled with limited memory creates a capacity issue. There is also a scalability issue because increasing the number of serving machines increases query latency and computation costs. This occurs as a result of the search being done in each machine, and latency increases with the increased number of aggregating candidates. To address these challenges, we propose SPTAG++, a distributed ANNS system. In this talk, we’ll discuss SPTAG++, which is now integrated into production to support hundreds of billions-scale vector searches in production with millisecond response time and more than ten thousand queries per second.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
- Track:
- The Future of Search & Recommendation
- Date:
- Speakers:
- Qi Chen
- Affiliation:
- Microsoft Research Asia
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Qi Chen
Principal Researcher
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The Future of Search & Recommendation
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Keynote: Universal search and recommendation
Speakers:- Paul Bennett
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Research talk: Learning and pretraining strategies for dense retrieval in search and beyond
Speakers:- Chenyan Xiong
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Research talk: Is phrase retrieval all we need?
Speakers:- Danqi Chen
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Research talk: Summarizing information across multiple documents and modalities
Speakers:- Subhojit Som
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Panel: The future of search and recommendation: Beyond web search
Speakers:- Eric Horvitz,
- Nitin Agrawal,
- Soumen Chakrabati
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Panel: Causality in search and recommendation systems
Speakers:- Emre Kiciman,
- Amit Sharma,
- Dean Eckles
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