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arXiv:2406.14920 · The Journal of Chemical Physics

Extending GPU-accelerated Gaussian integrals in the TeraChem software package to f type orbitals: Implementation and applications

Yuanheng Wang, Diptarka Hait, Katherine Grace Johnson, O. Jonathan Fajen, J. H. Zhang, Rubén Darío Guerrero, Todd J. Martı́nez

Published Nov 6, 2024• 19 citations
arXivHTMLPDF

Abstract

The increasing availability of graphics processing units (GPUs) for scientific computing has prompted interest in accelerating quantum chemical calculations through their use. However, the complexity of integral kernels for high angular momentum basis functions often limits the utility of GPU implementations with large basis sets or for metal containing systems. In this work, we report the implementation of f function support in the GPU-accelerated TeraChem software package through the development of efficient kernels for the evaluation of Hamiltonian integrals. The high efficiency of the resulting code is demonstrated through density functional theory (DFT) calculations on increasingly large organic molecules and transition metal complexes, as well as coupled cluster singles and doubles calculations on water clusters. Preliminary investigations into Ni(I) catalysis with DFT and the photochemistry of MnH(CH3) with complete active space self-consistent field are also carried out. Overall, our GPU-accelerated software appears to be well-suited for fast simulation of large transition metal containing systems, as well as organic molecules.

DOI
10.1063/5.0233523

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The increasing availability of graphics processing units (GPUs) for scientific computing has prompted interest in accelerating quantum chemical calculations through their use. However, the complexity of integral kernels for high angular momentum basis functions often limits the utility of GPU implementations with large basis sets or for metal containing systems. In this work, we report the implementation of f function support in the GPU-accelerated TeraChem software package through the development of efficient kernels for the evaluation of Hamiltonian integrals. The high efficiency of the resulting code is demonstrated through density functional theory (DFT) calculations on increasingly large organic molecules and transition metal complexes, as well as coupled cluster singles and doubles calculations on water clusters. Preliminary investigations into Ni(I) catalysis with DFT and the photochemistry of MnH(CH3) with complete active space self-consistent field are also carried out. Overall, our GPU-accelerated software appears to be well-suited for fast simulation of large transition metal containing systems, as well as organic molecules.
Coupled cluster with singles, doubles, and perturbative triples (CCSD(T)) often provides ground state correlation energies within ″chemical accuracy", but it suffers from high computational cost and steep scaling with system size. We present a GPU-accelerated implementation of CCSD(T) in the TeraChem software package. The new implementation achieves state-of-the-art performance, enabling the calculation of the (T) correction for a system with 63 atoms and more than 1000 basis functions in a little under 8 h on a single node. Additionally, we demonstrate the utility of our optimized implementation for the rapid calculation of full CCSD(T)/CBS stacking energies for all ten unique DNA base pair stacked tetramers. We expect that the TeraChem CCSD(T) implementation will enable the rapid calculation of high-level data that was not previously accessible in a reasonable time frame.
Developed over the past decade, TeraChem is an electronic structure and ab initio molecular dynamics software package designed from the ground up to leverage graphics processing units (GPUs) to perform large-scale ground and excited state quantum chemistry calculations in the gas and the condensed phase. TeraChem's speed stems from the reformulation of conventional electronic structure theories in terms of a set of individually optimized high-performance electronic structure operations (e.g., Coulomb and exchange matrix builds, one- and two-particle density matrix builds) and rank-reduction techniques (e.g., tensor hypercontraction). Recent efforts have encapsulated these core operations and provided language-agnostic interfaces. This greatly increases the accessibility and flexibility of TeraChem as a platform to develop new electronic structure methods on GPUs and provides clear optimization targets for emerging parallel computing architectures.
In this article, we present an effective approach to calculate quantum chemical two-electron integrals over basis sets consisting of Gaussian-type basis functions on graphical processing unit (GPU). Our framework generates several different variants called routes to the same integral problem with different integral algorithms (McMurchie-Davidson, Head-Gordon-Pople, and Rys) and precision. Each route is benchmarked on more GPU architectures, and with this data, a model is fitted to select the best available route for an integral task given a GPU architecture. Moreover, this approach supports the computation of high angular momentum orbitals up to g effectively on GPU, tested up to cc-pVQZ-sized basis sets. Rigorous analysis is shown regarding the effectiveness of our method. Molecule simulations with several basis sets are measured using NVIDIA GTX 1080 Ti, NVIDIA P100, and NVIDIA V100 cards.
The dynamic rearrangement of a molecule at finite temperature is not captured by a single static input geometry to a quantum chemical calculation. Multilevel workflows have established themselves as standard procedures to capture the dependence of the molecule’s configuration on computed properties. Each step within this workflow uses an electronic structure theory method that features the optimal computational time to accuracy ratio. For exploratory steps the computational expense of a method needs to be minimal as several thousand energy and nuclear gradient evaluations are needed. Semiempirical tight binding methods, such as GFN2-xTB are often used for such purposes. However, these methods usually lack the accuracy to rank conformers relative to each other within chemical accuracy (2 kcal/mol). Therefore, so-called “low-cost” DFT methods are commonly used for geometry optimizations and conformer ranking. The combination of (semi)empirical correction models with small basis sets leads to good accuracy at moderate computational cost. However, even these highly efficient methods reach the limits of feasibility for very large systems (> 200 atoms). This work proposes solutions to partially alleviate those restrictions and allow routine calculations of these system sizes outside of large high-performance computing facilities. The key to unlock those calculations is to combine tailored algorithmic developments with consumer-grade GPUs. A GPU-enabled linear algebra library is developed to accelerate further development and make the entry to GPU-accelerated software development easier. To demonstrate the ease of implementation, the addition of Mulliken-approximated exchange to GFN2-xTB will be shown. In most semiempirical methods, the general eigenvalue solver algorithm becomes the computational bottleneck, dominating the walltime with increasing system size. A density matrix purification algorithm is tailored to give up to six-fold acceleration of GFN2-xTB calculations using a mixed-precision approach. Lastly, 3c-methods are added to the GPU-accelerated TeraChem software package. In combination with the “mixed-precision” scheme for computing the electron repulsion integrals, this addition decreases the runtime of DFT calculations by up to an order of magnitude on mid-level consumer hardware. The overall goal of these projects is to make GPU-accelerated calculations in quantum chemistry more available. This achieve by exposing algorithmic developments that are performant on reasonably priced hardware and making GPU computing more accessible to non-experts.
We present a GPU-accelerated software package for the evaluation of analytic two-electron energy and gradient contributions in Hartree-Fock (HF) and Density Functional theory (DFT) calculations. The implementation is provided as a Python library with a C++ backend, enabling straightforward integration into modern computational chemistry and drug-discovery workflows. The code supports single-point energy and nuclear gradient evaluations on both single- and multi-GPU systems, and employs MPI-based parallelization with dynamic load balancing in multi-node environments. We report comprehensive benchmarks demonstrating favorable scaling with respect to system size, as well as high throughput for batched evaluations relevant to molecular dynamics, geometry optimization, and large-scale virtual screening. Parallel execution of a single system was carried out on up to 24 A100 GPUs. The implementation builds on optimized GPU-enabled variants of the LibintX and GauXC libraries to efficiently compute density-fitted Coulomb, semi-numerical exchange, and exchange--correlation contributions.
We present a GPU-accelerated software package for the evaluation of analytic two-electron energy and gradient contributions in Hartree-Fock (HF) and Density Functional theory (DFT) calculations. The implementation is provided as a Python library with a C++ backend, enabling straightforward integration into modern computational chemistry and drug-discovery workflows. The code supports single-point energy and nuclear gradient evaluations on both single- and multi-GPU systems, and employs MPI-based parallelization with dynamic load balancing in multi-node environments. We report comprehensive benchmarks demonstrating favorable scaling with respect to system size, as well as high throughput for batched evaluations relevant to molecular dynamics, geometry optimization, and large-scale virtual screening. Parallel execution of a single system was carried out on up to 24 A100 GPUs. The implementation builds on optimized GPU-enabled variants of the LibintX and GauXC libraries to efficiently compute density-fitted Coulomb, semi-numerical exchange, and exchange--correlation contributions.