Myths and Legends in High-Performance Computing

• URL: http://arxiv.org/abs/2301.02432v3
• Date: Wed, 25 Oct 2023 01:01:14 GMT
• Title: Myths and Legends in High-Performance Computing
• Authors: Satoshi Matsuoka, Jens Domke, Mohamed Wahib, and Aleksandr Drozd, Torsten Hoefler
• Abstract summary: We discuss certain myths and legends that are folklore among members of the high-performance computing community. We believe they represent the zeitgeist of the current era of massive change, driven by the end of many scaling laws. While some laws end, new directions are emerging, such as algorithmic scaling or novel architecture research.
• Score: 63.31942195960354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this thought-provoking article, we discuss certain myths and legends that are folklore among members of the high-performance computing community. We gathered these myths from conversations at conferences and meetings, product advertisements, papers, and other communications such as tweets, blogs, and news articles within and beyond our community. We believe they represent the zeitgeist of the current era of massive change, driven by the end of many scaling laws such as Dennard scaling and Moore's law. While some laws end, new directions are emerging, such as algorithmic scaling or novel architecture research. Nevertheless, these myths are rarely based on scientific facts, but rather on some evidence or argumentation. In fact, we believe that this is the very reason for the existence of many myths and why they cannot be answered clearly. While it feels like there should be clear answers for each, some may remain endless philosophical debates, such as whether Beethoven was better than Mozart. We would like to see our collection of myths as a discussion of possible new directions for research and industry investment.

Related papers

• Can Community Notes Replace Professional Fact-Checkers? [49.5332225129956]
  Policy changes by Twitter/X and Meta signal a shift away from partnerships with fact-checking organisations. Our analysis reveals that community notes cite fact-checking sources up to five times more than previously reported.
  arXiv  Detail & Related papers  (2025-02-19T22:26:39Z)
• NewsEdits 2.0: Learning the Intentions Behind Updating News [74.84017890548259]
  As events progress, news articles often update with new information: if we are not cautious, we risk propagating outdated facts. In this work, we hypothesize that linguistic features indicate factual fluidity, and that we can predict which facts in a news article will update using solely the text of a news article.
  arXiv  Detail & Related papers  (2024-11-27T23:35:23Z)
• Existential Conversations with Large Language Models: Content, Community, and Culture [11.573256071600722]
  Large language models can engage users on a wide variety of topics, including philosophy, spirituality, and religion. We trace likely sources, both ancient and modern, for the extensive repertoire of images, myths, metaphors, and conceptual esoterica that the language model draws on. We consider the larger societal impacts of such engagements with LLMs.
  arXiv  Detail & Related papers  (2024-11-20T11:35:22Z)
• Explainability and Hate Speech: Structured Explanations Make Social Media Moderators Faster [72.84926097773578]
  We investigate the effect of explanations on the speed of real-world moderators. Our experiments show that while generic explanations do not affect their speed and are often ignored, structured explanations lower moderators' decision making time by 7.4%.
  arXiv  Detail & Related papers  (2024-06-06T14:23:10Z)
• Do Large Language Models Know about Facts? [60.501902866946]
  Large language models (LLMs) have recently driven striking performance improvements across a range of natural language processing tasks. We aim to evaluate the extent and scope of factual knowledge within LLMs by designing the benchmark Pinocchio. Pinocchio contains 20K diverse factual questions that span different sources, timelines, domains, regions, and languages.
  arXiv  Detail & Related papers  (2023-10-08T14:26:55Z)
• Nobody Wants to Work Anymore: An Analysis of r/antiwork and the Interplay between Social and Mainstream Media during the Great Resignation [9.299167002524653]
  r/antiwork is a Reddit community that focuses on the discussion of worker exploitation, labour rights and related left-wing political ideas. In late 2021, r/antiwork became the fastest growing community on Reddit, coinciding with what the mainstream media began referring to as the Great Resignation. We investigate how the r/antiwork community was affected by the exponential increase in subscribers and the media coverage that chronicled its rise.
  arXiv  Detail & Related papers  (2022-10-14T13:27:14Z)
• Technology and Consciousness [0.0]
  We report on a series of eight workshops held in the summer of 2017 on the topic "technology and consciousness" The workshops covered many subjects but the overall goal was to assess the possibility of machine consciousness. An appendix outlines the topics of each workshop and provides abstracts of the talks delivered.
  arXiv  Detail & Related papers  (2022-07-17T23:23:01Z)
• Fact-driven Logical Reasoning for Machine Reading Comprehension [82.58857437343974]
  We are motivated to cover both commonsense and temporary knowledge clues hierarchically. Specifically, we propose a general formalism of knowledge units by extracting backbone constituents of the sentence. We then construct a supergraph on top of the fact units, allowing for the benefit of sentence-level (relations among fact groups) and entity-level interactions.
  arXiv  Detail & Related papers  (2021-05-21T13:11:13Z)
• Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks across Languages [60.00219873112454]
  We investigate the hypothesis that the extent to which one obtains information on a given topic through Wikipedia depends on the language in which it is consulted. Since Wikipedia is a central part of the web-based information landscape, this indicates a language-related, linguistic bias. The article builds a bridge between reading research, educational science, Wikipedia research and computational linguistics.
  arXiv  Detail & Related papers  (2020-08-05T11:11:55Z)
• Legends: Folklore on Reddit [0.4924126492174801]
  We introduce Reddit legends, a collection of venerated old posts that have become famous on Reddit. We show that Reddit legends can indeed be considered as folklore and that they are amendable to systematic text-based approaches.
  arXiv  Detail & Related papers  (2020-07-01T20:55:41Z)
• Social laser model for the Bandwagon effect: generation of coherent information waves [0.0]
  We show that this socio-psychic phenomenon can be modeled on the basis of the recently developed it social laser theory The paper contains minimum of mathematics and it can be readable by researchers working in psychology, cognitive, social, and political sciences.
  arXiv  Detail & Related papers  (2020-04-27T09:36:23Z)
• Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
  We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms. We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features. We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
  arXiv  Detail & Related papers  (2020-04-20T20:00:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.