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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>An argument in favor of strong scaling for deep neural networks with small datasets · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1807.09161/</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/1807.09161/feed.xml" rel="self" type="application/rss+xml" /><item><title>An argument in favor of strong scaling for deep neural networks with small datasets — mentioned by kvathupo</title><link>https://news.ycombinator.com/item?id=32671818</link><guid isPermaLink="false">urn:hackerpapers:mention:1807.09161:32671818</guid><pubDate>Thu, 01 Sep 2022 04:03:29 GMT</pubDate><description>&lt;p&gt;Although this seems to only affect the Chinese and Russian governments (as opposed to researchers), I&amp;#x27;m generally against economic brinkmanship of this variety. It just festers resentment, and doesn&amp;#x27;t seem to achieve much: of what little I could find and scan in an hour, it appears that Nvidia GPUs strong-scale pretty well for neural networks ([1], [2]). Although this depends heavily on the NN architecture and training algorithm, Ampere architectures _seem_ to suffer from similar levels of atomic contention as Volta architectures [3]. That said, the throughput for half-precision floating…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1807.09161/"&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>
