Publications | Kalman Szenes

2024

Stochastic and Low-Scaling Techniques: General Discussion

Ali Alavi, Marcus Allen, Kemal Atalar, Timothy C. Berkelbach, George H. Booth, Hugh G. A. Burton, Garnet Kin-Lic Chan, Luca Craciunescu, Don Danilov, Werner Dobrautz, Francesco A. Evangelista, Maria-Andreea Filip, Emmanuel Giner, Gabriel Greene-Diniz, Andreas Grüneis, Yang Guo, Gaurav Harsha, Basil Ibrahim, Venkat Kapil, Daniel Kats, Peter J. Knowles, Marie-Bernadette Lepetit, Ke Liao, Giovanni Li Manni, Pierre-François Loos, Erika Magnusson, Antoine Marie, Carlos Mejuto-Zaera, Frank Neese, Arman Nejad, Verena A. Neufeld, Katarzyna Pernal, Felix Plasser, Andrew W. Prentice, Visagan Ravindran, Umatur Rehman, Markus Reiher, Christian Schilling, Gustavo Scuseria, James Shee, Benjamin X. Shi, Kalman Szenes, David P. Tew, Alex J. W. Thom, Zikuan Wang, Dominika Zgid, Andrew Zhu, and Martijn A. Zwijnenburg

Faraday Discussion on Correlated Electronic Structure, London, UK, 2024

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@article{alaviStochasticLowscalingTechniques2024,
  title = {Stochastic and Low-Scaling Techniques: General Discussion},
  author = {Alavi, Ali and Allen, Marcus and Atalar, Kemal and Berkelbach, Timothy C. and Booth, George H. and Burton, Hugh G. A. and Chan, Garnet Kin-Lic and Craciunescu, Luca and Danilov, Don and Dobrautz, Werner and Evangelista, Francesco A. and Filip, Maria-Andreea and Giner, Emmanuel and {Greene-Diniz}, Gabriel and Gr{\"u}neis, Andreas and Guo, Yang and Harsha, Gaurav and Ibrahim, Basil and Kapil, Venkat and Kats, Daniel and Knowles, Peter J. and Lepetit, Marie-Bernadette and Liao, Ke and Li Manni, Giovanni and Loos, Pierre-Fran{\c c}ois and Magnusson, Erika and Marie, Antoine and {Mejuto-Zaera}, Carlos and Neese, Frank and Nejad, Arman and Neufeld, Verena A. and Pernal, Katarzyna and Plasser, Felix and Prentice, Andrew W. and Ravindran, Visagan and Rehman, Umatur and Reiher, Markus and Schilling, Christian and Scuseria, Gustavo and Shee, James and Shi, Benjamin X. and Szenes, Kalman and Tew, David P. and Thom, Alex J. W. and Wang, Zikuan and Zgid, Dominika and Zhu, Andrew and Zwijnenburg, Martijn A.},
  year = {2024},
  pages = {10.1039.D4FD90042A},
  doi = {10.1039/D4FD90042A},
  copyright = {All rights reserved},
  journal = {Faraday Discussion on Correlated Electronic Structure},
  location = {{London, UK}},
}

Striking the right balance of encoding electron correlation in the Hamiltonian and the wavefunction ansatz

Kalman Szenes, Maximilian Mörchen, Paul Fischill, and Markus Reiher

Faraday Discussion on Correlated Electronic Structure, London, UK, 2024

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Multi-configurational electronic structure theory delivers the most versatile approximations to many-electron wavefunctions, flexible enough to deal with all sorts of transformations, ranging from electronic excitations, to open-shell molecules and chemical reactions. Multi-configurational models are therefore essential to establish universally applicable, predictive ab initio methods for chemistry. Here, we present a discussion of explicit correlation approaches which address the nagging problem of dealing with static and dynamic electron correlation in multi-configurational active-space approaches. We review the latest developments and then point to their key obstacles. Our discussion is supported by new data obtained with tensor network methods. We argue in favor of simple electron-only correlator expressions that may allow one to define transcorrelated models in which the correlator does not bear a dependence on molecular structure.
@article{Szenes2024, author = {Szenes, Kalman and Mörchen, Maximilian and Fischill, Paul and Reiher, Markus}, title = {{Striking the right balance of encoding electron correlation in the Hamiltonian and the wavefunction ansatz}}, journal = {Faraday Discussion on Correlated Electronic Structure}, year = {2024}, doi = {10.1039/D4FD00060A}, location = {{London, UK}}, }
Domain-Specific Implementation of High-Order Discontinuous Galerkin Methods in Spherical Geometry

Kalman Szenes, Niccolò Discacciati, Luca Bonaventura, and William Sawyer

Computer Physics Communications, 2024

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In recent years, domain-specific languages (DSLs) have achieved significant success in large-scale efforts to reimplement existing meteorological models in a performance portable manner. The dynamical cores of these models are based on finite difference and finite volume schemes, and existing DSLs are generally limited to supporting only these numerical methods. In the meantime, there have been numerous attempts to use high-order Discontinuous Galerkin (DG) methods for atmospheric dynamics, which are currently largely unsupported in main-stream DSLs. In order to link these developments, we present two domain-specific languages which extend the existing GridTools (GT) ecosystem to high-order DG discretization. The first is a C++-based DSL called G4GT, which, despite being no longer supported, gave us the impetus to implement extensions to the subsequent Python-based production DSL called GT4Py to support the operations needed for DG solvers. As a proof of concept, the shallow water equations in spherical geometry are implemented in both DSLs, thus providing a blueprint for the application of domain-specific languages to the development of global atmospheric models. We believe this is the first GPU-capable DSL implementation of DG in spherical geometry. The results demonstrate that a DSL designed for finite difference/volume methods can be successfully extended to implement a DG solver, while preserving the performance-portability of the DSL.
@article{szenesDomainspecificImplementationHighorder2024, title = {Domain-Specific Implementation of High-Order {{Discontinuous Galerkin}} Methods in Spherical Geometry}, author = {Szenes, Kalman and Discacciati, Niccolò and Bonaventura, Luca and Sawyer, William}, date = {2024-02-01}, year = {2024}, journaltitle = {Computer Physics Communications}, journal = {Computer Physics Communications}, volume = {295}, pages = {108993}, issn = {0010-4655}, doi = {10.1016/j.cpc.2023.108993}, url = {https://www.sciencedirect.com/science/article/pii/S0010465523003387}, urldate = {2023-11-12}, keywords = {Discontinuous Galerkin methods,Domain-specific languages,GPU programming}, }

2023

SC’23
High-Performance and Programmable Attentional Graph Neural Networks with Global Tensor Formulations

Maciej Besta, Pawel Renc, Robert Gerstenberger, Paolo Sylos Labini, Alexandros Ziogas, Tiancheng Chen, Lukas Gianinazzi, Florian Scheidl, Kalman Szenes, Armon Carigiet, Patrick Iff, Grzegorz Kwasniewski, Raghavendra Kanakagiri, Chio Ge, Sammy Jaeger, Jarosław Wąs, Flavio Vella, and Torsten Hoefler

Supercomputing, Denver, CO, USA, 2023

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Graph attention models (A-GNNs), a type of Graph Neural Networks (GNNs), have been shown to be more powerful than simpler convolutional GNNs (C-GNNs). However, A-GNNs are more complex to program and difficult to scale. To address this, we develop a novel mathematical formulation, based on tensors that group all the feature vectors, targeting both training and inference of A-GNNs. The formulation enables straightforward adoption of communication-minimizing routines, it fosters optimizations such as vectorization, and it enables seamless integration with established linear algebra DSLs or libraries such as GraphBLAS. Our implementation uses a data redistribution scheme explicitly developed for sparse-dense tensor operations used heavily in GNNs, and fusing optimizations that further minimize memory usage and communication cost. We ensure theoretical asymptotic reductions in communicated data compared to the established message-passing GNN paradigm. Finally, we provide excellent scalability and speedups of even 4–5x over modern libraries such as Deep Graph Library.
@article{bestaHighPerformanceProgrammableAttentional2023, title = {High-{{Performance}} and {{Programmable Attentional Graph Neural Networks}} with {{Global Tensor Formulations}}}, author = {Besta, Maciej and Renc, Pawel and Gerstenberger, Robert and Sylos Labini, Paolo and Ziogas, Alexandros and Chen, Tiancheng and Gianinazzi, Lukas and Scheidl, Florian and Szenes, Kalman and Carigiet, Armon and Iff, Patrick and Kwasniewski, Grzegorz and Kanakagiri, Raghavendra and Ge, Chio and Jaeger, Sammy and Wąs, Jarosław and Vella, Flavio and Hoefler, Torsten}, date = {2023-11-11}, year = {2023}, publisher = {{Association for Computing Machinery}}, location = {{Denver, CO, USA}}, doi = {10.1145/3581784.3607067}, url = {https://doi.org/10.1145/3581784.3607067}, urldate = {2023-11-04}, journal = {Supercomputing}, }