用於計算機的熱力學：科學與工程的新領域

演講人

David Wolpert，聖塔菲研究所/麻省理工學院，IEEE Fellow

時間

2016年12月16日（周五）上午11：00-12：00

地點

清華大學 FIT 樓1-312（可從清華大學東南門正門進入，左手第一棟即是FIT樓）

演講摘要

計算耗能佔美國當今總能耗的5%。而一個現代高性能計算中心有50%的成本用於購買能源。這些能源最終都將轉化為熱量，不會做任何物理的功。谷歌將自己的很多數據伺服器放在河流旁邊，以利用河水冷卻伺服器。還有個例子，現在建造超級計算機的重大挑戰之一是防止計算機被自己產生的熱量熔化。所以，計算熱力學在工程學上有非常重要的作用，能解決人類的重大問題。

幸運的是，過去20年里非平衡統計物理學發生了一場革命。一些極為強大的工具誕生了，這些工具可以分析遠離平衡態的系統（如計算機）的熱力學性質和基本界限。我們也因此目睹了一個全新科學工程領域的誕生：計算的現代熱力學。

在演講中我會介紹一些相關的進展。我還會著重討論在結合了熱力學和傳統計算機科學後，考慮了空間時間妥協的理論計算機科學會有怎樣的進步。我還將專門介紹一些引入了機器學習的計算熱力學。

Abstract

5% of the current energy usage in the US goes to computation. Moreover, 50% of the lifetime budget of a modern high-performance computing center is to pay the energy bill. All of that energy ultimately results only in heat, with no physical work done at all.

Indeed, Google has placed some of their data servers next to rivers - in order to use the water in the river to cool the servers, i.e., to remove the heat they generate. As another example, one of the major challenges facing current efforts to build exascale computers is how to keep them from melting. Clearly the thermodynamics of computation has major engineering consequences, which are becoming a huge challenge to humanity.

Fortunately, the last two decades have seen a revolution in nonequilibrium statistical physics. This has resulted in some extremely powerful tools for anayzing the thermodynamic properties and fundamental limits of far-from-equilibrium systems - like computers. As a result we are witnessing the genesis of a new field of science and engineering: a modern thermodynamics of computation.

In this talk I discuss these developments. I also highlight some of the major extensions to theoretical computer science that are arising as we combine thermodynamic considerations with the traditional computer science concerns of space / time tradeoffs. In particular, I present some novel issues in the thermodynamics of computation that involve machine learning.

Biography

David Wolpert is an IEEE fellow, is the author of three books and more than 200 papers, has three patents, is an associate editor at more than half a dozen journals, and has received numerous awards. He has more than 17,000 citations in a wide range of fields, including physics, machine learning, game theory, information theory, the therodynamics of computation, and distributed optimization. In particular, his machine learning technique of stacking was instrumental in both winning entries for the Netflix competiton, and his papers on the no free lunch theorems jointly have more than 7,000 citations .