Insider Brief
- The Association for Computing Machinery, named an eight-member team drawn from American and Indian institutions as the winner of the 2023 ACM Gordon Bell Prize for a quantum modeling project.
- The name of the winning project was “Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic Alloys”.
- The ACM Gordon Bell Prize tracks the progress of parallel computing and rewards innovation in applying high-performance computing to challenges in science, engineering, and large-scale data analytics.
PRESS RELEASE — ACM, the Association for Computing Machinery, named an eight-member team drawn from American and Indian institutions as the winner of the 2023 ACM Gordon Bell Prize for the project, “Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic Alloys.”
The members of the team are: Sambit Das (University of Michigan), Bikash Kanungo (University of Michigan), Vishal Subramanian, (University of Michigan), Gourab Panigrahi (Indian Institute of Science, Bangalore), Phani Motamarri (Indian Institute of Science, Bangalore), David Rogers (Oakridge National Laboratory), Paul M. Zimmerman (University of Michigan), and Vikram Gavini (University of Michigan).
Molecular dynamics is a process by which computer simulations are used to better understand the movements of atoms and molecules within a system. Ab initio (Latin for “from the beginning”) is a branch of molecular dynamics that has been shown to be an especially effective technique when applied to important problems in physics and chemistry—including efforts to better understand microscopic mechanisms, gain new insights in materials science, and prove out experimental data.
Despite the successes of ab initio approaches in a wide range of computer simulations, the team notes that efforts to employ quantum mechanical ab initio methods to predict materials’ properties has not been able to achieve quantum accuracy and scale on the powerful supercomputers needed to perform these simulations. In their abstract to their Gordon Bell Prize-winning project the authors write, “ Ab initio electronic-structure has remained dichotomous between achievable accuracy and length-scale. Quantum Many-Body (QMB) methods realize quantum accuracy but fail to scale.”
To address this challenge, the Gordon Bell Prize-winning team developed a framework that combines the accuracy provided by QMB methods with the efficiency of Density-Functional Theory (DFT) to access larger length scales at quantum accuracy—a goal that existing approaches have not been able to achieve.
The 2023 ACM Gordon Bell Prize-winning team writes, “We demonstrate a paradigm shift in DFT that not only provides an accuracy commensurate with QMB methods in ground-state energies, but also attains an unprecedented performance of 659.7 PFLOPS (43.1% peak FP64 performance) on 619,124 electrons using 8,000 GPU nodes of Frontier supercomputer.”
The ACM Gordon Bell Prize tracks the progress of parallel computing and rewards innovation in applying high-performance computing to challenges in science, engineering, and large-scale data analytics. The award was presented during the International Conference for High Performance Computing, Networking, Storage and Analysis (SC22), which was held in Dallas, Texas.
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