85 citations · 134 across the 12 of their papers we have counts for
14 papers
A Portable and Versatile Limited-Memory BFGS Implementation in PETSc/TAO
Hansol Suh, Tobin Isaac, Alp Dener +3
The limited-memory BFGS (L-BFGS) Hessian update scheme is the critical kernel in many quasi-Newton optimization algorithms. The most common approach to implementing L-BFGS uses $2m…
Scalable Cross-Facility Federated Learning for Scientific Foundation Models on Multiple Supercomputers
Yijiang Li, Zilinghan Li, Kyle Chard +4
Artificial Intelligence for scientific applications increasingly requires training large models on data that cannot be centralized due to privacy constraints, data sovereignty, or…
PETSc/TAO Developments for GPU-Based Early Exascale Systems
Richard Tran Mills, Mark Adams, Satish Balay +9
The Portable Extensible Toolkit for Scientific Computation (PETSc) library provides scalable solvers for nonlinear time-dependent differential and algebraic equations and for numer…
2022 Review of Data-Driven Plasma Science
Rushil Anirudh, Rick Archibald, M. Salman Asif +60
Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data…
The PETSc Community Is the Infrastructure
Mark Adams, Satish Balay, Oana Marin +13
The communities who develop and support open source scientific software packages are crucial to the utility and success of such packages. Moreover, these communities form an import…
ExaWorks: Workflows for Exascale
Aymen Al-Saadi, Dong H. Ahn, Yadu Babuji +12
Exascale computers will offer transformative capabilities to combine data-driven and learning-based approaches with traditional simulation applications to accelerate scientific dis…
Improving I/O Performance for Exascale Applications through Online Data Layout Reorganization
Lipeng Wan, Axel Huebl, Junmin Gu +12
The applications being developed within the U.S. Exascale Computing Project (ECP) to run on imminent Exascale computers will generate scientific results with unprecedented fidelity…
Workflows Community Summit: Advancing the State-of-the-art of Scientific Workflows Management Systems Research and Development
Rafael Ferreira da Silva, Henri Casanova, Kyle Chard +55
Scientific workflows are a cornerstone of modern scientific computing, and they have underpinned some of the most significant discoveries of the last decade. Many of these workflow…
Scalable Multigrid-based Hierarchical Scientific Data Refactoring on GPUs
Jieyang Chen, Lipeng Wan, Xin Liang +10
Rapid growth in scientific data and a widening gap between computational speed and I/O bandwidth makes it increasingly infeasible to store and share all data produced by scientific…
Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations
Alexander Brace, Igor Yakushin, Heng Ma +7
Machine learning (ML)-based steering can improve the performance of ensemble-based simulations by allowing for online selection of more scientifically meaningful computations. We p…
The PetscSF Scalable Communication Layer
Junchao Zhang, Jed Brown, Satish Balay +7
PetscSF, the communication component of the Portable, Extensible Toolkit for Scientific Computation (PETSc), is designed to provide PETSc's communication infrastructure suitable fo…
Toward Performance-Portable PETSc for GPU-based Exascale Systems
Richard Tran Mills, Mark F. Adams, Satish Balay +11
The Portable Extensible Toolkit for Scientific computation (PETSc) library delivers scalable solvers for nonlinear time-dependent differential and algebraic equations and for numer…
Training neural networks under physical constraints using a stochastic augmented Lagrangian approach
Alp Dener, Marco Andres Miller, Randy Michael Churchill +2
We investigate the physics-constrained training of an encoder-decoder neural network for approximating the Fokker-Planck-Landau collision operator in the 5-dimensional kinetic fusi…
Encoder-decoder neural network for solving the nonlinear Fokker-Planck-Landau collision operator in XGC
M. A. Miller, R. M. Churchill, A. Dener +3
An encoder-decoder neural network has been used to examine the possibility for acceleration of a partial integro-differential equation, the Fokker-Planck-Landau collision operator.…