A Look at the 2023 Gordon Bell Prize Finalists

As in previous years, the 2023 Gordon Bell Prize (GBP) drew a number of exciting submissions. From them, the Association for Computing Machinery (ACM) GBP Award Committee selected six finalists. Chester Gordon Bell (pictured above) is the namesake of the prestigious award that honors outstanding achievements in HPC.

The GBP is awarded to the most valuable scientific computation as demonstrated using state-of-the-art software and hardware technologies on world-leading supercomputers. The GBP represents various aspects of evaluation, such as the importance of target problems, performance optimization, maximum utilization of target system performance, and knowledge given for widely spread platforms. This year, the Gordon Bell finalists’ work spans various applications, including materials science, fluid dynamics, nuclear simulation, seismic processing, and biomolecular simulations. The hardware platforms also include world-class systems: Frontier (ORNL, USA), new Sunway System (SC Wuxi, China), LUMI (EuroHPC/CSC, Finland), Leonardo (EuroHPC/Cineca, Italy), Cerebras CS-2 (KAUST, Saudi Arabia), and Perlmutter (NERSC, USA).

The following summarizes this year’s Gordon Bell finalists via brief descriptions of the work. Please note that the results or system sizes will not be finalized before the teams’ final submissions in August. The ACM Gordon Bell Prize award winner will be announced as part of a special ceremony at SC23 this November in Denver.

Taisuke Boku

Professor/Director of the Center for Computational Sciences, University of Tsukuba

Finalist 1

In this work, the team developed a mixed method to combine density function theory (DFT) and the quantum many body (QMB) problem using a machine learning technique. The effort achieves high accuracy of calculation and affords large-scale modeling with the inverse-DFT that links the QMB method to DFT. They realized the ground-stage energy calculation while keeping the accuracy commensurate with QMB, using more than 60% of resources on the Frontier supercomputer housed within the Oak Ridge Leadership Computing Facility.

Finalist 2

The team developed a new large eddy simulation code to solve compressible flows in turbomachinery. They applied it to NASA’s grand challenge problem with a high-order unstructured solver for a high-pressure turbine cascade of 1.69 billion mesh elements and 865 billion degrees of freedom. The code has been calculated on Wuxi’s new Sunway supercomputer with extreme many-cores per node, up to 19.2 million cores, where each computation node consists of 384 calculation cores and six control cores.

Finalist 3

The team simulated an advanced nuclear reactor system coupling radiation transport with heat and fluid simulation, including the high-fidelity, high-resolution Monte-Carlo code, Shift, and the computational fluid dynamics code, NekRS. Nek5000/RS was implemented on ORNL’s Frontier system and achieved 1 billion spectral elements and 350 billion degrees of freedom, while Shift achieved very high weak-scaling on 8192 system nodes. As a result, they calculated six reactions in 214,896 fuel pin regions below 1% statistical error, yielding first-of-a-kind resolution for a Monte Carlo transport application.

Finalist 4

The team developed a high-fidelity spectral-element code, Neko, which is essential for unprecedented large-scale direct numerical simulations of fully developed turbulence—all while maintaining high-performance portability on GPU-accelerated platforms. They applied a GPU-optimized preconditioner with task overlapping for the pressure-Poisson equation and in situ data compression. They also conducted initial runs of Rayleigh–Bénard convection at extreme scale on the LUMI and Leonardo supercomputers with up to 16,384 GPUs via a sophisticated workflow control.

Finalist 5

This work exploits the high-memory bandwidth of artificial intelligence (AI)-customized Cerebras CS-2 systems for seismic processing by leveraging the low-rank matrix approximation to fit the problem on SRAM (static random-access memory) wafer-scale hardware and use many wave-equation-based algorithms that rely on multidimensional convolution operators. As a result, the team implemented a standard seismic benchmark dataset into the small local memories of Cerebras processing elements, extrapolating a worst-case load-balanced whole application execution to 48 CS-2 systems on 35,784,000 processing elements. This is a significant example of applications run on AI-customized architectures that can enable a new generation of seismic algorithms.

Finalist 6

The group developed the Allegro architecture to bridge the accuracy-speed tradeoff of atomistic simulations and enable the description of dynamics in structures of unprecedented complexity at quantum fidelity. This is achieved through a combination of innovative model architecture, massive parallelization, and model implementations optimized for efficient GPU utilization. Allegro’s scalability is illustrated by a nanoseconds-long stable simulation of protein dynamics and up to 44-million atom structure of a complete, all-atom, explicitly solvated HIV capsid on the Perlmutter system at the National Energy Research Scientific Computing Center. They achieved strong scaling up to 100 million atoms.

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