特邀嘉賓

Keynote1

Junfeng Yang is Professor of Computer Science, Member of the Data Science Institute, and co-Director of the Software Systems Lab at Columbia University. Yang’s research centers on building reliable, secure, and fast software systems. Today’s software systems are large, complex, and plagued with errors, some of which have caused critical system failures, breaches, and performance degradation. Yang has invented techniques, algorithms, and tools to analyze, test, debug, monitor, and optimize real-world software, including Android, Linux, production systems at Microsoft, machine learning systems, and self-driving platforms, benefiting hundreds of millions of users. His research has resulted in numerous vulnerability patches to real-world systems, practical adoption at the largest technology companies, and press coverage at Scientific American, The Atlantic, The Register, Communications of ACM, and other news outlets. Yang received BS in Computer Science from Tsinghua University and MS and PhD in Computer Science from Stanford University. He won the Sloan Research Fellowship and the Air Force Office of Scientific Research Young Investigator Program Award, both in 2012; the National Science Foundation CAREER award in 2011; the inaugural Rock Star Award of the Association of Chinese Scholars in Computing in 2019; and Best Paper Awards at the USENIX Symposium on Operating System Design and Implementation in 2004, the ACM Symposium on Operating Systems Principles in 2017, and the USENIX Annual Technical Conference in 2021.

報告題目：Debugging Performance Issues in Modern Desktop Applications

摘要： Modern desktop applications involve many asynchronous, concurrent interactions that make performance issues difficult to diagnose. Although prior work has used causal tracing for debugging performance issues in distributed systems, we find that these techniques suffer from high inaccuracies for desktop applications. In this talk, I will present Argus, a fast, effective causal tracing tool for debugging performance anomalies in desktop applications. Argus introduces a novel notion of strong and weak edges to explicitly model and annotate trace graph ambiguities, a new beam-search-based diagnosis algorithm to select the most likely causal paths in the presence of ambiguities, and a new way to compare causal paths across normal and abnormal executions. We have implemented Argus across multiple versions of macOS and evaluated it on 12 infamous spinning pinwheel issues in popular macOS applications. Argus diagnosed the root causes for all issues, 10 of which were previously unknown, some of which have been open for several years. This work won a Best Paper award in USENIX ATC 2021. It is joint with Lingmei Weng (lead PhD student, graduating next academic year), Ryan Peng Huang, and Jason Nieh.

Keynote2

譚光明，研究員、博導、中科院計算技術(shù)研究所高性能計算機研究中心主任。國家杰出青年基金獲得者，參與了曙光系列高性能計算機包括曙光4000/5000/6000/7000系統(tǒng)研制。發(fā)表學術(shù)論文100余篇，包括CCF A類論文（TC、SC、PPoPP）和Nature子刊等，曾任IEEE TPDS編委和國際會議（SC、PPoPP）等程序委員。曾獲得國家科技進步獎二等獎、盧嘉錫青年人才獎和全國向上向善好青年稱號。

報告題目：高性能計算性能工程

摘要： 高性能計算領域的核心命題是關(guān)于如何滿足應用性能需求，與一般性計算問題相比而言，性能通常是第一優(yōu)先級考慮的指標。總體上而言，影響性能的諸多因素主要包括：硬件設計（流水線、向量寬度、Cache大小等）、算法模型（復雜度等）、實現(xiàn)方式（編程語言、數(shù)據(jù)結(jié)構(gòu)、庫的版本等）、代碼生成（編譯器）、系統(tǒng)配置（操作系統(tǒng)的選擇等）和執(zhí)行環(huán)境（親和性選擇、資源分配和系統(tǒng)噪音等）。在真實的運行系統(tǒng)中，這些性能因素之間不是獨立正交，而是相互影響形成一個非常復雜龐大的優(yōu)化空間。在單純以軟件工程驅(qū)動的高性能計算軟件棧設計中，人們?yōu)榱俗非蟾叩纳a(chǎn)效率，通過分層模塊設計把錯綜復雜的性能因素“粗暴”地割裂開，在通用硬件性能提升放緩的情況下，所謂的軟件“腫脹”導致的性能瓶頸問題就凸顯出來。這種性能損失對以性能為第一優(yōu)先目標的高性能計算而言顯得尤為突出，因此，在繼高性能計算的硬件工程和軟件工程技術(shù)系統(tǒng)發(fā)展多年之后，本報告試圖提倡高性能計算性能工程的研究，以系統(tǒng)發(fā)展性能工程技術(shù)，應對高性能計算軟硬件棧在后摩爾時代的挑戰(zhàn)。

Keynote3

Lili Qiu is an Assistant Managing Director at Microsoft Research Asia and a Professor at Computer Science Dept. in UT Austin. She got M.S. and PhD degrees in Computer Science from Cornell University in 1999 and 2001, respectively. After graduation, she spent 2001-2004 as a researcher at System & Networking Group in Microsoft Research Redmond. She joined UT Austin in 2005, and has founded a vibrant research group working on Internet and wireless networks at UT. She is an ACM Fellow and IEEE Fellow. She also got an NSF CAREER award and Google Faculty Research Award, and best paper awards at ACM MobiSys'18 and IEEE ICNP'17. She advised a PhD dissertation that won SIGMOBILE best dissertation award in 2020.

報告題目：Acoustic Sensing and Applications

摘要： Video games, Virtual Reality (VR), Augmented Reality (AR), and Smart appliances (e.g., smart TVs and drones) all call for a new way for users to interact and control them. Motivated by this observation, we have developed a series of novel acoustic sensing technologies by transmitting specifically designed signals or using signals naturally arising from the environments. We further develop a few interesting applications on top of our motion tracking technology such as a follow-me drone and acoustic imaging on mobile phones.

Keynote4

Shan Lu is a Professor in the Department of Computer Science at the University of Chicago. Her research focuses on detecting, diagnosing, and fixing functional and performance bugs in software systems. Shan is an ACM Distinguished Member (2019 class) and an Alfred P. Sloan Research Fellow (2014). Her co-authored papers have won distinguished paper and influential paper awards at ASPLOS, SOSP, OSDI, FAST, ICSE, FSE, CHI, and PLDI. Shan currently serves as the Chair of ACM-SIGOPS, and the Vice Chair of ACM SIG Governing Board Executive Committee. She served as the technical program co-chair for ASPLOS 2022, OSDI 2020, APSys 2018, and USENIX ATC 2015

報告題目： 15 Years of Learning from Mistakes in Building System Software

摘要： Bugs severely threaten the correctness and efficiency of software. With our system software growing its complexity, bugs in system software also evolve, imposing different challenges over the years. In this talk, we look back at our study of concurrency bugs in multi-threaded software, which was done 15 years ago and recently won ASPLOS Influential Paper Award, as well as various bug studies that we conducted over the years about distributed systems, industry cloud systems, database systems, machine learning systems, etc. We discuss the lessons that we have learned, as well as the new challenges faced by today's system building.

Keynote5

侯銳，中國科學院信息工程研究所信息安全國家重點實驗室副主任、研究員、博士生導師，獲得國家杰青、優(yōu)青項目資助。長期從事處理器芯片架構(gòu)設計及芯片安全等方面的研究工作。

報告題目： 處理器安全分支預測器

摘要： 在數(shù)字化日益普及的今日，數(shù)據(jù)中心處理器芯片安全問題愈發(fā)重要。尤其是在云端，處理器面臨著眾多的安全風險。我們以處理器中性能提升關(guān)鍵模塊——分支預測器——為切入點，分別從更新策略、內(nèi)容存儲、索引映射三個方面對傳統(tǒng)分支預測器設計進行了解構(gòu)，并提出了一系列安全增強機制，實現(xiàn)了分支預測器的安全重構(gòu)。

Keynote6

黃克驥博士，華為存儲領域8級技術(shù)專家，數(shù)據(jù)存儲產(chǎn)品線首席架構(gòu)師，負責華為存儲產(chǎn)品的整體架構(gòu)規(guī)劃和技術(shù)演進，是華為存儲產(chǎn)品競爭力實現(xiàn)業(yè)界領先的領軍人物。黃克驥博士具有超過18年ICT從業(yè)經(jīng)驗和超過15年存儲領域研究經(jīng)驗，持續(xù)深耕技術(shù)創(chuàng)新和根科技構(gòu)建，在存儲領域積累了深厚的技術(shù)功底，先后負責華為賽門鐵克云存儲產(chǎn)品、華為第一代分布式NAS產(chǎn)品和大數(shù)據(jù)存儲產(chǎn)品、融合存儲NAS產(chǎn)品、全閃存存儲產(chǎn)品、華為第一代存儲平臺、華為云存儲和數(shù)據(jù)架構(gòu)等多個產(chǎn)品及大型架構(gòu)的規(guī)劃和設計，奠定了華為存儲產(chǎn)品競爭力成為國內(nèi)第一并進入Gartner領導者象限的堅實基礎。

報告題目：基于DPU的存儲創(chuàng)新

摘要： 數(shù)據(jù)密集型業(yè)務的快速發(fā)展驅(qū)動DPU成為繼CPU和GPU之后的第三大算力，業(yè)界圍繞DPU進行創(chuàng)新也成為新的熱點。華為依托在存儲領域多年的技術(shù)積累，也基于DPU進行了從硬件和OS到虛擬化、大數(shù)據(jù)、數(shù)據(jù)庫等場景加速的一些創(chuàng)新實踐，旨在通過DPU與存儲結(jié)合為用戶帶來數(shù)據(jù)處理和存儲效率的倍數(shù)級提升。