2021年图灵奖公布！72岁的美国科学家 Jack Dongarra 获奖

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作者 | Ailleurs

编辑 | 陈彩娴

刚刚，2021年计算机领域的最高奖项——图灵奖公布！美国计算机科学家 Jack J. Dongarra 获奖，以表彰他在高性能计算领域的卓越成就。

根据 ACM 的介绍，Dongarra 的算法和软件推动了高性能计算的发展，对人工智能、计算机图形学等多个计算科学领域均产生了重大的影响。

他在数值算法和库方面做出了开创性的贡献，使得高性能计算软件能够跟上四十多年来的指数级硬件更新。

图灵奖被称为「计算机领域的诺贝尔奖」，由美国计算机协会（ACM）于 1966 年设立，目的之一是为了纪念世界计算机科学的先驱艾伦·图灵（A.M. Turing），每年评选出在计算机领域作出重大贡献的一到两名科学家，奖励100万美元，由谷歌全额赞助。

1

Jack Dongarra 是谁？

Jack J. Dongarra 生于 1950 年 7 月 18 日，自1989年以来便在田纳西大学电气工程和计算机科学系担任特聘教授，还是美国橡树岭国家实验室计算机科学和数学部的杰出研究人员。自2007年以来，他还担任曼彻斯特大学数学学院的图灵研究员，同时在莱斯大学计算机科学系担任兼职教授。

他的求学经历如下：

• 1972 年获得芝加哥州立大学数学学士学位
• 1973 年获得伊利诺伊理工学院计算机科学硕士学位
• 1980 年获得新墨西哥大学应用数学哲学博士学位，师从美国工程院院士 Cleve Moler

在博士毕业后、加入田纳西大学大学前，他一直在阿贡国家实验室工作。

回顾 Jack J. Dongarra 的研究生涯，可谓风光无限：他曾获得 IEEE 计算机先锋奖、SIAM/ACM 计算科学与工程奖和 ACM/IEEE 肯肯尼迪奖，同时还是 ACM Fellow、IEEE Fellow、SIAM Fellow、AAAS Fellow、ISC Fellow 与 IETI Fellow，真·Fellow大满贯。

此外，他还是美国国家工程院院士与英国皇家学会的外籍院士。

看谷歌学术，他的被引数超过了 11 万，h-index 超过了 130：

2

他的研究贡献

据ACM官网通报，Dongarra 通过对线性代数运算的高效数值算法、并行计算编程机制和性能评估工具的贡献引领了高性能计算的世界。

近四十年来，摩尔定律使硬件性能呈指数级增长。与此同时，虽然大多数软件未能跟上这些硬件进步的步伐，但高性能数值软件却做到了——这在很大程度上归功于 Dongarra 的算法、优化技术和生产质量的软件实施。

这些贡献奠定了一个框架，可以使科学家和工程师在大数据分析、医疗保健、可再生能源、天气预报、基因组学和经济学等领域取得重要发现和改变游戏规则的创新。Dongarra 的工作还有助于促进计算机体系结构的跨越式发展，并支持计算机图形学和深度学习的革命。

Dongarra 的主要贡献是创建了开源软件库和标准，这些软件库和标准采用线性代数作为中间语言，可以被各种应用程序使用。这些库是为单处理器、并行计算机、多核节点和每个节点的多个 GPU 编写的。Dongarra 的库还引入了许多重要的创新，包括自动调整、混合精度算术和批处理计算。

作为高性能计算的领先研究者，Dongarra 带领该领域说服硬件供应商优化这些方法，并说服软件开发人员在他们的工作中以他的开源库为目标。最终，这些努力导致基于线性代数的软件库在从笔记本电脑到世界上最快的超级计算机等机器上实现了几乎普遍的高性能科学和工程计算。这些库对于该领域的发展至关重要——使功能越来越强大的计算机能够解决具有计算挑战性的问题。

ACM 的主席Gabriele Kotsis 表示：

「除了对打破新记录的兴趣之外，高性能计算一直是科学发现的主要工具。HPC 创新也蔓延到许多不同的计算领域，推动了我们整个领域的发展。Jack Dongarra 在指导这一领域的成功发展中发挥了核心作用。他的开创性工作可以追溯到 1979 年，至今他仍是 HPC 领域最重要且积极参与的领导者之一。他的职业生涯无疑体现了图灵奖对『具有持久重要性的重大贡献』的认可。」

谷歌的 Jeff Dean 也评价：

「Jack Dongarra 的工作从根本上改变并推动了科学计算。他在世界上使用最频繁的数值库的核心所做的深入而重要的工作是科学计算各个领域的基础，帮助推进了从药物发现到天气预报、航空航天工程和其他数十个领域的发展，他专注于表征广泛的计算机已经为计算机体系结构带来重大进步，（使之）非常适合数值计算。」

四十多年来，Dongarra 一直是 LINPACK、BLAS、LAPACK、ScaLAPACK、PLASMA、MAGMA 和 SLATE 等多个库的主要实施者或首席研究员。这些库是为单处理器、并行计算机、多核节点和每个节点的多个 GPU 编写的。他的软件库几乎普遍用于在从笔记本电脑到世界上最快的超级计算机等机器上进行高性能科学和工程计算。

这些库体现了许多深刻的技术创新，例如：

• 自动调整：从他获得「2016 年超算会议时间测试奖 ATLAS」的项目来看，Dongarra 开创了自动查找算法参数的方法，这些算法参数能够产生接近最佳效率的线性代数内核，通常优于供应商提供的代码。
• 混合精度算术：在他被 2006 年SC会议接收的论文“Exploiting the Performance of 32 bit Floating Point Arithmetic in Obtaining 64 bit Accuracy”中，Dongarra 率先利用浮点算术的多种精度来更快地提供准确的解决方案。最近的 HPL-AI 基准（该基准在世界顶级超级计算机上实现了前所未有的性能水平）测试展示，这项工作在机器学习应用中发挥了重要作用，该基准在世界顶级超级计算机上实现了前所未有的性能水平。
• 批量计算：Dongarra 开创了将大型密集矩阵的计算划分为可独立和并行计算的范式，常被用于模拟、建模和数据分析。根据他在 2016 年的论文“Performance, design, and autotuning of batched GEMM for GPUs”，Dongarra 领导了用于此类计算的「批量 BLAS 标准」的开发，并应用于软件库 MAGMA 和 SLATE 中。

Dongarra 在上述工作中与许多国际学者进行合作，通过不断开发新技术以最大限度地提高性能和便携性，同时使用最先进的技术保持数值可靠的结果，始终扮演了创新驱动力的角色。

他领导的其他研究还包括消息传递接口 (MPI)，MPI 是并行计算架构中可移植消息传递的事实标准；以及性能 API (PAPI)，它提供了一个接口，允许从异构系统收集和合成来自组件的性能。他帮助创建的标准（例如 MPI、LINPACK 基准测试和 Top500 超级计算机列表）支撑着从天气预报到气候变化再到分析大型物理实验数据的计算任务。

英文好的可以略去如上中文：

Dongarra’s Algorithms and Software Fueled the Growth of High-Performance Computing and Had Significant Impacts in Many Areas of Computational Science from AI to Computer Graphics

ACM, the Association for Computing Machinery, today named Jack J. Dongarra recipient of the 2021 ACM A.M. Turing Award for pioneering contributions to numerical algorithms and libraries that enabled high performance computational software to keep pace with exponential hardware improvements for over four decades. Dongarra is a University Distinguished Professor of Computer Science in the Electrical Engineering and Computer Science Department at the University of Tennessee. He also holds appointments with Oak Ridge National Laboratory and the University of Manchester.

The ACM A.M. Turing Award, often referred to as the “Nobel Prize of Computing,” carries a $1 million prize, with financial support provided by Google, Inc. It is named for Alan M. Turing, the British mathematician who articulated the mathematical foundation and limits of computing.

Dongarra has led the world of high-performance computing through his contributions to efficient numerical algorithms for linear algebra operations, parallel computing programming mechanisms, and performance evaluation tools. For nearly forty years, Moore’s Law produced exponential growth in hardware performance. During that same time, while most software failed to keep pace with these hardware advances, high performance numerical software did—in large part due to Dongarra’s algorithms, optimization techniques, and production-quality software implementations.

These contributions laid a framework from which scientists and engineers made important discoveries and game-changing innovations in areas including big data analytics, healthcare, renewable energy, weather prediction, genomics, and economics, to name a few. Dongarra’s work also helped facilitate leapfrog advances in computer architecture and supported revolutions in computer graphics and deep learning.

Dongarra’s major contribution was in creating open-source software libraries and standards which employ linear algebra as an intermediate language that can be used by a wide variety of applications. These libraries have been written for single processors, parallel computers, multicore nodes, and multiple GPUs per node. Dongarra’s libraries also introduced many important innovations including autotuning, mixed precision arithmetic, and batch computations.

As a leading ambassador of high-performance computing, Dongarra led the field in persuading hardware vendors to optimize these methods, and software developers to target his open-source libraries in their work. Ultimately, these efforts resulted in linear algebra-based software libraries achieving nearly universal adoption for high performance scientific and engineering computation on machines ranging from laptops to the world’s fastest supercomputers. These libraries were essential in the growth of the field—allowing progressively more powerful computers to solve computationally challenging problems.

“Today’s fastest supercomputers draw headlines in the media and excite public interest by performing mind-boggling feats of a quadrillion calculations in a second,” explains ACM President Gabriele Kotsis. “But beyond the understandable interest in new records being broken, high performance computing has been a major instrument of scientific discovery. HPC innovations have also spilled over into many different areas of computing and moved our entire field forward. Jack Dongarra played a central part in directing the successful trajectory of this field. His trailblazing work stretches back to 1979, and he remains one of the foremost and actively engaged leaders in the HPC community. His career certainly exemplifies the Turing Award’s recognition of ‘major contributions of lasting importance.’”

“Jack Dongarra's work has fundamentally changed and advanced scientific computing,” said Jeff Dean, Google Senior Fellow and SVP of Google Research and Google Health. “His deep and important work at the core of the world's most heavily used numerical libraries underlie every area of scientific computing, helping advance everything from drug discovery to weather forecasting, aerospace engineering and dozens more fields, and his deep focus on characterizing the performance of a wide range of computers has led to major advances in computer architectures that are well suited for numeric computations.”

Dongarra will be formally presented with the ACM A.M. Turing Award at the annual ACM Awards Banquet, which will be held this year on Saturday, June 11 at the Palace Hotel in San Francisco.

SELECT TECHNICAL CONTRIBUTIONS

For over four decades, Dongarra has been the primary implementor or principal investigator for many libraries such as LINPACK, BLAS, LAPACK, ScaLAPACK, PLASMA, MAGMA, and SLATE. These libraries have been written for single processors, parallel computers, multicore nodes, and multiple GPUs per node. His software libraries are used, practically universally, for high performance scientific and engineering computation on machines ranging from laptops to the world’s fastest supercomputers.

These libraries embody many deep technical innovations such as:

Autotuning: through his 2016 Supercomputing Conference Test of Time award-winning ATLAS project, Dongarra pioneered methods for automatically finding algorithmic parameters that produce linear algebra kernels of near-optimal efficiency, often outperforming vendor-supplied codes.

Mixed precision arithmetic: In his 2006 Supercomputing Conference paper, “Exploiting the Performance of 32 bit Floating Point Arithmetic in Obtaining 64 bit Accuracy,” Dongarra pioneered harnessing multiple precisions of floating-point arithmetic to deliver accurate solutions more quickly. This work has become instrumental in machine learning applications, as showcased recently in the HPL-AI benchmark, which achieved unprecedented levels of performance on the world’s top supercomputers.

Batch computations: Dongarra pioneered the paradigm of dividing computations of large dense matrices, which are commonly used in simulations, modeling, and data analysis, into many computations of smaller tasks over blocks that can be calculated independently and concurrently. Based on his 2016 paper, “Performance, design, and autotuning of batched GEMM for GPUs,” Dongarra led the development of the Batched BLAS Standard for such computations, and they also appear in the software libraries MAGMA and SLATE.

Dongarra has collaborated internationally with many people on the efforts above, always in the role of the driving force for innovation by continually developing new techniques to maximize performance and portability while maintaining numerically reliable results using state of the art techniques.  Other examples of his leadership include the Message Passing Interface (MPI) the de-facto standard for portable message-passing on parallel computing architectures, and the Performance API (PAPI), which provides an interface that allows collection and synthesis of performance from components of a heterogeneous system. The standards he helped create, such as MPI, the LINPACK Benchmark, and the Top500 list of supercomputers, underpin computational tasks ranging from weather prediction to climate change to analyzing data from large scale physics experiments.

Biographical Background

Jack J. Dongarra has been a University Distinguished Professor at the University of Tennessee and a Distinguished Research Staff Member at the Oak Ridge National Laboratory since 1989. He has also served as a Turing Fellow at the University of Manchester (UK) since 2007. Dongarra earned a B.S. in Mathematics from Chicago State University, an M.S. in Computer Science from the Illinois Institute of Technology, and a Ph.D. in Applied Mathematics from the University of New Mexico.

Dongarra’s honors include the IEEE Computer Pioneer Award, the SIAM/ACM Prize in Computational Science and Engineering, and the ACM/IEEE Ken Kennedy Award. He is a Fellow of ACM, the Institute of Electrical and Electronics Engineers (IEEE), the Society of Industrial and Applied Mathematics (SIAM), the American Association for the Advancement of Science (AAAS), the International Supercomputing Conference (ISC), and the International Engineering and Technology Institute (IETI). He is a member of the National Academy of Engineering and a foreign member of the British Royal Society.

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The A.M. Turing Award, the ACM's most prestigious technical award, is given for major contributions of lasting importance to computing.

This site celebrates all the winners since the award's creation in 1966. It contains biographical information, a description of their accomplishments, straightforward explanations of their fields of specialization, and text or video of their A. M. Turing Award Lecture.

A.M TURING

The A.M. Turing Award, sometimes referred to as the "Nobel Prize of Computing," was named in honor of Alan Mathison Turing (1912–1954), a British mathematician and computer scientist. He made fundamental advances in computer architecture, algorithms, formalization of computing, and artificial intelligence. Turing was also instrumental in British code-breaking work during World War II.

参考链接：

https://amturing.acm.org

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