<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2509.06046/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2509.06046/feed.xml" rel="self" type="application/rss+xml" /><item><title>DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers — mentioned by kingcauchy</title><link>https://news.ycombinator.com/item?id=47415335</link><guid isPermaLink="false">urn:hackerpapers:mention:2509.06046:47415335</guid><pubDate>Tue, 17 Mar 2026 17:00:04 GMT</pubDate><description>&lt;p&gt;Not strictly google but microsoft/bing too, here&amp;#x27;s the top ones from my notes: https://arxiv.org/abs/2410.14452 spfresh, https://arxiv.org/abs/2111.08566 spann, https://arxiv.org/abs/2405.12497 rabitq, https://arxiv.org/abs/2509.06046 diskann, I have a variety of blogs that I used too and reference implementations! It&amp;#x27;s a Rabit[Q]uantized Hierchical Balanced Clustering algorithm we use for the vector index and we use a chunked segment index for the sparse index if you&amp;#x27;re curious! Happy to discuss more!&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2509.06046/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.DC</category></item></channel></rss>
