剛剛，2021年計算機領域的最高獎項——圖靈獎公布！美國計算機科學家 Jack J. Dongarra 獲獎，以表彰他在高性能計算領域的卓越成就。

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根據 ACM 的介紹，Dongarra 的算法和軟件推動了高性能計算的發展，對人工智能、計算機圖形學等多個計算科學領域均產生了重大的影響。

他在數值算法和庫方面做出了開創性的貢獻，使得高性能計算軟件能夠跟上四十多年來的指數級硬件更新。

圖靈獎被稱為「計算機領域的諾貝爾獎」，由美國計算機協會（ACM）于 1966 年設立，目的之一是為了紀念世界計算機科學的先驅艾倫·圖靈（A.M. Turing），每年評選出在計算機領域作出重大貢獻的一到兩名科學家，獎勵100萬美元，由谷歌全額贊助。

1

Jack Dongarra 是誰？

Jack J. Dongarra 生于 1950 年 7 月 18 日，自1989年以來便在田納西大學電氣工程和計算機科學系擔任特聘教授，還是美國橡樹嶺國家實驗室計算機科學和數學部的杰出研究人員。自2007年以來，他還擔任曼徹斯特大學數學學院的圖靈研究員，同時在萊斯大學計算機科學系擔任兼職教授。

他的求學經歷如下：

• 1972 年獲得芝加哥州立大學數學學士學位

• 1973 年獲得伊利諾伊理工學院計算機科學碩士學位

• 1980 年獲得新墨西哥大學應用數學哲學博士學位，師從美國工程院院士 Cleve Moler

在博士畢業后、加入田納西大學大學前，他一直在阿貢國家實驗室工作。

回顧 Jack J. Dongarra 的研究生涯，可謂風光無限：他曾獲得 IEEE 計算機先鋒獎、SIAM/ACM 計算科學與工程獎和 ACM/IEEE 肯肯尼迪獎，同時還是 ACM Fellow、IEEE Fellow、SIAM Fellow、AAAS Fellow、ISC Fellow 與 IETI Fellow，真·Fellow大滿貫。

此外，他還是美國國家工程院院士與英國皇家學會的外籍院士。

看谷歌學術，他的被引數超過了 11 萬，h-index 超過了 130：

2

他的研究貢獻

據ACM官網通報，Dongarra 通過對線性代數運算的高效數值算法、并行計算編程機制和性能評估工具的貢獻引領了高性能計算的世界。

近四十年來，摩爾定律使硬件性能呈指數級增長。與此同時，雖然大多數軟件未能跟上這些硬件進步的步伐，但高性能數值軟件卻做到了——這在很大程度上歸功于 Dongarra 的算法、優化技術和生產質量的軟件實施。

這些貢獻奠定了一個框架，可以使科學家和工程師在大數據分析、醫療保健、可再生能源、天氣預報、基因組學和經濟學等領域取得重要發現和改變游戲規則的創新。Dongarra 的工作還有助于促進計算機體系結構的跨越式發展，并支持計算機圖形學和深度學習的革命。

Dongarra 的主要貢獻是創建了開源軟件庫和標準，這些軟件庫和標準采用線性代數作為中間語言，可以被各種應用程序使用。這些庫是為單處理器、并行計算機、多核節點和每個節點的多個 GPU 編寫的。Dongarra 的庫還引入了許多重要的創新，包括自動調整、混合精度算術和批處理計算。

作為高性能計算的領先研究者，Dongarra 帶領該領域說服硬件供應商優化這些方法，并說服軟件開發人員在他們的工作中以他的開源庫為目標。最終，這些努力導致基于線性代數的軟件庫在從筆記本電腦到世界上最快的超級計算機等機器上實現了幾乎普遍的高性能科學和工程計算。這些庫對于該領域的發展至關重要——使功能越來越強大的計算機能夠解決具有計算挑戰性的問題。

ACM 的主席Gabriele Kotsis 表示：

「除了對打破新記錄的興趣之外，高性能計算一直是科學發現的主要工具。HPC 創新也蔓延到許多不同的計算領域，推動了我們整個領域的發展。Jack Dongarra 在指導這一領域的成功發展中發揮了核心作用。他的開創性工作可以追溯到 1979 年，至今他仍是 HPC 領域最重要且積極參與的領導者之一。他的職業生涯無疑體現了圖靈獎對『具有持久重要性的重大貢獻』的認可。」

谷歌的 Jeff Dean 也評價：

「Jack Dongarra 的工作從根本上改變并推動了科學計算。他在世界上使用最頻繁的數值庫的核心所做的深入而重要的工作是科學計算各個領域的基礎，幫助推進了從藥物發現到天氣預報、航空航天工程和其他數十個領域的發展，他專注于表征廣泛的計算機已經為計算機體系結構帶來重大進步，（使之）非常適合數值計算。」

四十多年來，Dongarra 一直是 LINPACK、BLAS、LAPACK、ScaLAPACK、PLASMA、MAGMA 和 SLATE 等多個庫的主要實施者或首席研究員。這些庫是為單處理器、并行計算機、多核節點和每個節點的多個 GPU 編寫的。他的軟件庫幾乎普遍用于在從筆記本電腦到世界上最快的超級計算機等機器上進行高性能科學和工程計算。

這些庫體現了許多深刻的技術創新，例如：

• 自動調整：從他獲得「2016 年超算會議時間測試獎 ATLAS」的項目來看，Dongarra 開創了自動查找算法參數的方法，這些算法參數能夠產生接近最佳效率的線性代數內核，通常優于供應商提供的代碼。

• 混合精度算術：在他被 2006 年SC會議接收的論文“Exploiting the Performance of 32 bit Floating Point Arithmetic in Obtaining 64 bit Accuracy”中，Dongarra 率先利用浮點算術的多種精度來更快地提供準確的解決方案。最近的 HPL-AI 基準（該基準在世界頂級超級計算機上實現了前所未有的性能水平）測試展示，這項工作在機器學習應用中發揮了重要作用，該基準在世界頂級超級計算機上實現了前所未有的性能水平。

• 批量計算：Dongarra 開創了將大型密集矩陣的計算劃分為可獨立和并行計算的范式，常被用于模擬、建模和數據分析。根據他在 2016 年的論文“Performance, design, and autotuning of batched GEMM for GPUs”，Dongarra 領導了用于此類計算的「批量 BLAS 標準」的開發，并應用于軟件庫 MAGMA 和 SLATE 中。

Dongarra 在上述工作中與許多國際學者進行合作，通過不斷開發新技術以最大限度地提高性能和便攜性，同時使用最先進的技術保持數值可靠的結果，始終扮演了創新驅動力的角色。

他領導的其他研究還包括消息傳遞接口 (MPI)，MPI 是并行計算架構中可移植消息傳遞的事實標準；以及性能 API (PAPI)，它提供了一個接口，允許從異構系統收集和合成來自組件的性能。他幫助創建的標準（例如 MPI、LINPACK 基準測試和 Top500 超級計算機列表）支撐著從天氣預報到氣候變化再到分析大型物理實驗數據的計算任務。

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.

參考鏈接：

https://amturing.acm.org

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