系庆讲座 | 第09场:未来计算的性能模型

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楼主 2018-12-05 16:00:52
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1958 - 2018


为了认真总结六十年来艰苦创业、勇攀高峰的发展历史,大力弘扬自强不息、努力拼搏的奋斗精神,努力建设清华风格、世界一流的计算机学科,清华大学计算机科学与技术系六十周年系庆筹备委员会讨论决定举办六十周年系庆主题纪念活动,活动主题拟定为“不忘初心,潜心计算一甲子;牢记使命,筑梦人类新百年”。

从5月开始,系庆学术报告活动将陆续开展。欢迎广大师生及系友持续关注,并到场聆听。



清华大学计算机科学与技术系

六十周年系庆

学术报告

第09场

时间:2018年6月11日 10:00-11:00

地点:东主楼10-103


摘要

We are close to a phase-change in the computing industry. The demand for computing power is steadily increasing with the advent of (deep) learning and other high-performance computing techniques. However, the end of Dennard scaling and Moore's law force us to design for parallelism and specialization to improve compute performance. The complexity of programming is enormous and we propose the use of mathematical models to understand the performance requirements of practical algorithms. We first show pitfalls of seemingly simple performance measurements, followed by a methodology to design close-to-optimal programs. We also showcase a mathematical system design methodology for high-performance networks. All these examples testify to the value of modeling in practical high-performance computing. We assume that a broader use  of these techniques and the development of a solid theory for parallel performance will lead to deep insights at many fronts.


 报告人简介 


Torsten Hoefler


ETH Zurich

Torsten Hoefler directs the Scalable Parallel Computing Laboratory (SPCL) at D-INFK ETH Zurich. He received his PhD degree in 2007 at Indiana University and started his first professor appointment in 2011 at the University of Illinois at Urbana-Champaign. Torsten has served as the lead for performance modeling and analysis in the US NSF Blue Waters project at NCSA/UIUC. Since 2013, he is a professor of computer science at ETH Zurich and has held visiting positions at Argonne National Laboratories, Sandia National Laboratories, and Microsoft Research Redmond (Station Q). Dr. Hoefler's research aims at understanding the performance of parallel computing systems ranging from parallel computer architecture through parallel programming to parallel algorithms. He is also active in the application areas of Weather and Climate simulations as well as Machine Learning with a focus on Distributed Deep Learning. In those areas, he has coordinated tens of funded projects and an ERC Starting Grant on Data-Centric Parallel Programming.




系庆期间将进行数十场相关学术报告。报告现场每人均可领取集章册进行打卡。累积听取系庆学术报告达到一定场次可获得神秘大奖!


投稿邮箱:thucs@tsinghua.edu.cn

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