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publications
Support CEED-enabled ECP applications in their preparation for Aurora/Frontier
Published in , 2020
CEED-MS35: Port and optimize the CEED software stack to Aurora/Frontier EA
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pplacerDC: a new scalable phylogenetic placement method
Published in Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, 2021
This paper presents a novel algorithm for phylogenetic placement that is scalable to large datasets. The general idea is to decompose a given tree using a centroid decomposition, and to apply recursively until the trees are small enough such that they may be solved using pplacer. From there, the results are merged together to produce a final placement.
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GPU algorithms for efficient exascale discretizations
Published in Parallel Computing, 2021
This paper presents the work of the Center for Efficient Exascale Discretizations (CEED) to develop efficient algorithms for exascale discretizations on GPUs.
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CEED-MS37: Port and optimize the CEED software stack to Aurora/Frontier EA
Published in , 2021
CEED-MS37: Port and optimize the CEED software stack to Aurora/Frontier EA
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Tuning Spectral Element Preconditioners for Parallel Scalability on GPUs
Published in SIAM PP 22 Proceeding, 2022
This paper is concerning runtime tuning spectral element preconditioners for parallel scalability on GPUs. A nascent tuning strategy is proposed to help alleviate the difficulty of choosing a reasonable preconditioner for a given problem. A variety of novel preconditioning schemes are also proposed, including $p$-multigrid with Chebyshev-accelerated Schwarz smoothers, low-order operator preconditioning, and even hybridizing the two approach by treating the low-order operator as the coarse grid operator.
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High-order algorithmic developments and optimizations for more robust exascale applications
Published in , 2022
High-order algorithmic developments and optimizations for more robust exascale applications
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Improve performance and capabilities of CEED-enabled ECP applications on Frontier/Aurora EA
Published in , 2022
High-order algorithmic developments and optimizations for more robust exascale applications
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Optimal Chebyshev Smoothers and One-sided V-cycles
Published in arXiv, 2022
This paper considers the use of 4th-kind and optimized 4th-kind Chebyshev, polynomials as accelerators for multigrid smoothing. These methods are developed in James Lottes’s excellent paper, Optimal polynomial smoothers for multigrid V-cycles. We extend the analysis of Lottes to the case of one-sided V-cycles, and show that, for large values of the multigrid approximation constant, smoothing with $2k$-order Chebyshev polynomials on one-side yields superior convergence to smoothing with $k$-order Chebyshev polynomials on both sides.
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NekRS, a GPU-Accelerated Spectral Element Navier–Stokes Solver
Published in Parallel Computing, 2022
This paper presents nekRS, a GPU-accelerated spectral element Navier–Stokes solver.
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Optimization of full-core reactor simulations on summit
Published in SC22: International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2022
This paper presents nekRS full-core reactor simulations on all of Summit.
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Support ECP applications in their exascale challenge problem runs
Published in , 2023
Support ECP applications in their exascale challenge problem runs
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talks
Scalable Chebyshev-Accelerated Schwarz Preconditioning for GPUs
Published:
Present novel $p$-MG preconditioner built on Chebyshev-accelerated additive Schwarz and restrictive additive Schwarz smoothers. These preconditioner techniques are now the default solvers utilized in nekRS, an open-source high-order CFD solver targeting GPUs.
Scalable Chebyshev-Accelerated Schwarz Preconditioning for GPUs
Published:
Present novel $p$-MG preconditioner built on Chebyshev-accelerated additive Schwarz and restrictive additive Schwarz smoothers. These preconditioner techniques are now the default solvers utilized in nekRS, an open-source high-order CFD solver targeting GPUs.
Tuning Spectral Element Preconditioners for Parallel Scalability on GPUs
Published:
Compare strong/weak scalability of various $p$-multigrid preconditioners and low-order operator preconditioning. A nascent tuning strategy is proposed to help alleviate the difficulty of choosing a reasonable preconditioner for a given problem.
Solver Development in nekRS: Optimal Chebyshev Smoothers and One-sided V-cycles
Published:
Demonstrate improved convergence rate using novel 4th-kind and optimal 4th-kind Chebyshev smoothers based on Lottes’s work Optimal polynomial smoothers for multigrid V-cycles. In addition, analyze the applicability of one-sided V-cycles to improve the convergence rate of Chebyshev smoothers in both a high-order $p$-multigrid and finite difference geometric multigrid applications.
Improving Parallel Scalability of High-Order Preconditioners on GPUs
Published:
Discuss improvements in parallel scalability of high-order preconditioners through James Lottes’s novel fourth-kind Chebyshev polynomial smoothers.
teaching
CS450, Spring 2019
Undergraduate course, UIUC, CS, 2019
Numerical Analysis (CS450)
- Instructor: Dr. Andreas Kloeckner
- Design unique problem sets:
- Optimization for inverse kinematics robotics problem
- Hold office hours
- Nominated for Teaching Assistant Award
CS450, Fall 2019
Undergraduate course, UIUC, CS, 2019
Numerical Analysis (CS450)
- Instructor: Dr. Luke Olson
- Design problem sets
- Hold office hours
CS555, Spring 2020
Undergraduate course, UIUC, CS, 2020
Numerical Methods for PDEs (CS555)
- Instructor: Dr. Andreas Kloeckner
- Design unique problem sets
- Hold office hours