Achieving performance portability in Gaussian basis set density functional theory on accelerator based architectures in NWChemEx

Williams‐Young, David B.; Bagusetty, Abhishek; Jong, Wibe A. de; Doerfler, Douglas W.; Dam, Hubertus J. J. van; Vázquez‐Mayagoitia, Álvaro; Windus, Theresa L.; Yang, Chao

doi:10.1016/j.parco.2021.102829

Cited by 10 publications

(3 citation statements)

References 53 publications

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“…In addition, domain-driven tensor frameworks − typically outperform generic tensor frameworks for computational chemistry workloads. As such, many chemistry-community driven libraries have been developed for low-level operations such as analytical (e.g., libint, libcint, and simint) and numerical (e.g., GauXC, − libGridXC) operator integrals, functional evaluation (libxc, XCfun, ExchCXX), and tensor algebra (e.g., TiledArray, , TAMM, and the Cyclops Tensor Framework (CTF)) to name a few. The development and optimization of these libraries is challenging; thus, it is of critical interest to the development of sustainable computational chemistry software that these kernels be implemented in a performance portable manner.…”

Section: Programming Models and Software Integrationmentioning

confidence: 99%

A Perspective on Sustainable Computational Chemistry Software Development and Integration

Di Felice,

Mayes,

Richard

et al. 2023

J. Chem. Theory Comput.

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The power of quantum chemistry to predict the ground and excited state properties of complex chemical systems has driven the development of computational quantum chemistry software, integrating advances in theory, applied mathematics, and computer science. The emergence of new computational paradigms associated with exascale technologies also poses significant challenges that require a flexible forward strategy to take full advantage of existing and forthcoming computational resources. In this context, the sustainability and interoperability of computational chemistry software development are among the most pressing issues. In this perspective, we discuss software infrastructure needs and investments with an eye to fully utilize exascale resources and provide unique computational tools for next-generation science problems and scientific discoveries.

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Section: Programming Models and Software Integrationmentioning

confidence: 99%

A Perspective on Sustainable Computational Chemistry Software Development and Integration

Di Felice,

Mayes,

Richard

et al. 2023

J. Chem. Theory Comput.

Self Cite

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“…As of this review, we have CUDA, HIP and SYCL ports of the density functional theory (DFT)-XC integrator. There has recently been a large structural refactor to allow for porting to new architectures (per the algorithmic design outlined in [12]) which allows the decoupling of high-level algorithmic workflows from the implementation details of performance critical kernels to be optimized for each architecture of interest. However, due to lack of resources, only the CUDA and HIP ports of the integrator have been ported in this new software infrastructure as of this report, the SYCL functionality will be ported over in the coming fiscal year.…”

Section: Dftmentioning

confidence: 99%

Application Results on Early Exascale Hardware

Siegel

Draeger

Deslippe

et al. 2022

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“…However, when reasonable large systems (≥1000 atoms) are studied, accurate wavefunction-based methods cannot be employed due to their high computational cost (above all, for excited state properties), although some progress has been recently achieved using graphical processing units [49] and localization procedures [50,51]. Thus, density functional theory (DFT) and time dependent (TD-) DFT, the latter required for excited state quantities, are usually chosen as a good compromise between accuracy and computational costs [10,[52][53][54][55][56][57][58][59][60][61][62].…”

Section: Introductionmentioning

confidence: 99%

An Expedited Route to Optical and Electronic Properties at Finite Temperature via Unsupervised Learning

et al. 2023

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Electronic properties and absorption spectra are the grounds to investigate molecular electronic states and their interactions with the environment. Modeling and computations are required for the molecular understanding and design strategies of photo-active materials and sensors. However, the interpretation of such properties demands expensive computations and dealing with the interplay of electronic excited states with the conformational freedom of the chromophores in complex matrices (i.e., solvents, biomolecules, crystals) at finite temperature. Computational protocols combining time dependent density functional theory and ab initio molecular dynamics (MD) have become very powerful in this field, although they require still a large number of computations for a detailed reproduction of electronic properties, such as band shapes. Besides the ongoing research in more traditional computational chemistry fields, data analysis and machine learning methods have been increasingly employed as complementary approaches for efficient data exploration, prediction and model development, starting from the data resulting from MD simulations and electronic structure calculations. In this work, dataset reduction capabilities by unsupervised clustering techniques applied to MD trajectories are proposed and tested for the ab initio modeling of electronic absorption spectra of two challenging case studies: a non-covalent charge-transfer dimer and a ruthenium complex in solution at room temperature. The K-medoids clustering technique is applied and is proven to be able to reduce by ∼100 times the total cost of excited state calculations on an MD sampling with no loss in the accuracy and it also provides an easier understanding of the representative structures (medoids) to be analyzed on the molecular scale.

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Achieving performance portability in Gaussian basis set density functional theory on accelerator based architectures in NWChemEx

Cited by 10 publications

References 53 publications

A Perspective on Sustainable Computational Chemistry Software Development and Integration

A Perspective on Sustainable Computational Chemistry Software Development and Integration

Application Results on Early Exascale Hardware

An Expedited Route to Optical and Electronic Properties at Finite Temperature via Unsupervised Learning

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