|2021 ACM Turing Award For Numerical Algorithms|
|Written by Sue Gee|
|Friday, 01 April 2022|
Jack Dongarra is the recipient of the 2021 ACM A.M. Turing Award for his contributions to efficient numerical algorithms for linear algebra operations, parallel computing programming mechanisms, and performance evaluation.
Considered to be the Nobel Prize of computing, the annual ACM A.M. Turing Award is worth $1 million and recognizes significant fundamental contributions to computing. Established in 1966, the award was named to honor Alan M. Turing and is the most prestigious of those made by the ACM (Association for Computing Machinery).
Jack Dongarra is a 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 with the University of Manchester, which was also Turing’s academic home.
The citation in respect of Jack Dongarra reads
For his 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’s major contribution was in creating open-source software libraries and standards which employ linear algebra as an intermediate language and that can be used by a wide variety of applications in many areas of computational science including data analytics, healthcare, renewable energy, weather prediction, genomics, and economics.
According to the ACM's post:
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. Dongarra recognized that linear algebra operations could be designed and implemented in a largely hardware-independent way by choosing suitable abstractions and optimization methods. His innovations have been key to mapping linear algebra operations efficiently to increasingly complex computer architectures.
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|Last Updated ( Friday, 01 April 2022 )|