Calculation of quantum chemical two-electron integrals by applying compiler technology on GPU
Tornai, Gábor János, István Ladjánszki, Ádám Rák, Gergely Kis, and György Cserey. “Calculation of Quantum Chemical Two-Electron Integrals by Applying Compiler Technology on GPU.” Journal of Chemical Theory and Computation 15, no. 10 (October 2019): 5319–31. https://doi.org/10.1021/acs.jctc.9b00560.
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.
Towards chemically accurate QM/MM simulations on GPUs
Jász, Ádám, Ádám Rák, István Ladjánszki, Gábor János Tornai, and György Cserey. “Towards Chemically Accurate QM/MM Simulations on GPUs.” Journal of Molecular Graphics and Modelling 96 (2020): 107536. https://doi.org/10.1016/j.jmgm.2020.107536
Computational chemistry simulations are extensively used to model natural phenomena. To maintain performance similar to molecular mechanics, but achieve comparable accuracy to quantum mechanical calculations, many researchers are using hybrid QM/MM methods. In this article we evaluate our GPU-accelerated ONIOM implementation by measurements on the crambin and HIV integrase proteins with different size QM model systems. We demonstrate that by using a larger QM region, a better energy accuracy can be achieved at the expense of simulation time. This trade-off is important to consider for the researcher running QM/MM calculations. Furthermore, we show that the ONIOM energy monotonically approaches the pure quantum mechanical energy of the whole system. The experiments are made feasible by utilizing the cutting-edge BrianQC quantum chemistry module for Hartree-Fock level SCF and our GPU-accelerated MMFF94 force field implementation for molecular mechanics calculations.
Classical molecular dynamics on graphics processing unit architectures
Jász, Ádám, Ádám Rák, István Ladjánszki, and György Cserey. “Classical Molecular Dynamics on Graphics Processing Unit Architectures.” WIREs Computational Molecular Science 10, no. 2 (May 2019). https://doi.org/10.1002/wcms.1444.
Molecular dynamics (MD) has experienced a significant growth in the recent decades. Simulating systems consisting of hundreds of thousands of atoms is a routine task of computational chemistry researchers nowadays. Thanks to the straightforwardly parallelizable structure of the algorithms, the most promising method to speed‐up MD calculations is exploiting the large‐scale processing power offered by the parallel hardware architecture of graphics processing units or GPUs. Programming GPUs is becoming easier with general‐purpose GPU computing frameworks and higher levels of abstraction. In the recent years, implementing MD simulations on graphics processors has gained a large interest, with multiple popular software packages including some form of GPU‐acceleration support. Different approaches have been developed regarding various aspects of the algorithms, with important differences in the specific solutions. Focusing on published works in the field of classical MD, we describe the chosen implementation methods and algorithmic techniques used for porting to GPU, as well as how recent advances of GPU architectures will provide even more optimization possibilities in the future.
Optimized GPU implementation of Merck molecular force field and universal force field
Jász, Ádám, Ádám Rák, István Ladjánszki, and György Cserey. “Optimized GPU Implementation of Merck Molecular Force Field and Universal Force Field.” Journal of Molecular Structure 1188 (2019): 227–33. https://doi.org/10.1016/j.molstruc.2019.04.007
In silico research is important for many fields of modern science, from small molecule thermochemistry to drug design. Published algorithms and implementations all strived for exploiting the actual technology and hardware in the best and most efficient way. In this article we demonstrate novel GPU implementations of the well known Merck Molecular Force Field (MMFF94) and Universal Force Field (UFF) algorithms which are near to utilize the theoretical peak performance of the GPU the software runs on. A double-precision speedup of 55× for MMFF94 and 140× for UFF is achieved, with the factor being 90× for the single-precision implementation of MMFF94. Tests were carried out on the MMFF94 validation suite and on various length generated peptide chains.
The BRUSH algorithm for two-electron integrals on GPU
Rák, Ádám, and György Cserey. “The BRUSH Algorithm for Two-Electron Integrals on GPU.” Chemical Physics Letters 622 (2015): 92–98. https://doi.org/10.1016/j.cplett.2015.01.023.
This Letter presents a new algorithmic method developed to evaluate two-electron repulsion integrals based on contracted Gaussian basis functions in a parallel way. This new algorithm scheme provides distinct SIMD (single instruction multiple data) optimized paths which symbolically transforms integral parameters into target integral algorithms. Our measurements indicate that the method gives a significant improvement over the CPU-friendly PRISM algorithm. The benchmark tests (evaluation of more than 108 integrals using the STO-3G basis set) of our GPU (NVIDIA GTX 780) implementation showed up to 750-fold speedup compared to a single core of Athlon II X4 635 CPU.