https://hackerpaper.kaipfister.de/p/2110.15032/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMThttps://news.ycombinator.com/item?id=29470528urn:hackerpapers:mention:2110.15032:29470528Tue, 07 Dec 2021 09:21:25 GMT<p>This article lists papers on GShard, presents background information and inspiration from the papers, and finally evaluates what else can be done to improve GShard from similar work that has been done in OneFlow. OneFlow Paper:https://arxiv.org/abs/2110.15032; Code:https://github.com/Oneflow-Inc/oneflow/ The paper of Gshard contains two main parts of work, one on parallel APIs and one on Mixture of experts. The former part is more interesting and I will only discuss this part. The contribution on parallel APIs is outlined clearly in the abstract of the paper: GShard is a module composed of a…</p><p><a href="https://hackerpaper.kaipfister.de/p/2110.15032/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.DChttps://news.ycombinator.com/item?id=29078828urn:hackerpapers:submission:2110.15032:29078828Tue, 02 Nov 2021 09:18:23 GMT<p>OneFlow: Redesign the Distributed Deep Learning Framework from Scratch</p><p></p><p>2 points; 0 discussion replies on HN. <a href="https://hackerpaper.kaipfister.de/p/2110.15032/">Paper and source history</a>. A submission is not an endorsement.</p>cs.DC