Coupled cluster theory on modern heterogeneous supercomputers

This study examines the computational challenges in elucidating intricate chemical systems, particularly through ab-initio methodologies. This work highlights the Divide-Expand-Consolidate (DEC) approach for coupled cluster (CC) theory—a linear-scaling, massively parallel framework—as a viable solution. Detailed scrutiny of the DEC framework reveals its extensive applicability for large chemical systems, yet it also acknowledges inherent limitations. To mitigate these constraints, the cluster perturbation theory is presented as an effective remedy. Attention is then directed towards the CPS (D-3) model, explicitly derived from a CC singles parent and a doubles auxiliary excitation space, for computing excitation energies. The reviewed new algorithms for the CPS (D-3) method efficiently capitalize on multiple nodes and graphical processing units, expediting heavy tensor contractions. As a result, CPS (D-3) emerges as a scalable, rapid, and precise solution for computing molecular properties in large molecular systems, marking it an efficient contender to conventional CC models

Distributed Memory, GPU Accelerated Fock Construction for Hybrid, Gaussian Basis Density Functional Theory

With the growing reliance of modern supercomputers on accelerator-based architectures such a GPUs, the development and optimization of electronic structure methods to exploit these massively parallel resources has become a recent priority. While significant strides have been made in the development of GPU accelerated, distributed memory algorithms for many-body (e.g. coupled-cluster) and spectral single-body (e.g. planewave, real-space and finite-element density functional theory [DFT]), the vast majority of GPU-accelerated Gaussian atomic orbital methods have focused on shared memory systems with only a handful of examples pursuing massive parallelism on distributed memory GPU architectures. In the present work, we present a set of distributed memory algorithms for the evaluation of the Coulomb and exact-exchange matrices for hybrid Kohn-Sham DFT with Gaussian basis sets via direct density-fitted (DF-J-Engine) and seminumerical (sn-K) methods, respectively. The absolute performance and strong scalability of the developed methods are demonstrated on systems ranging from a few hundred to over one thousand atoms using up to 128 NVIDIA A100 GPUs on the Perlmutter supercomputer.Comment: 45 pages, 9 figure

Development of highly efficient and accurate real-space integration methods for Hartree-Fock and hybrid density functional calculations

The central focus of molecular electronic structure theory is to find approximate solutions to the electronic Schrödinger equation for molecules, and as such represents an essential part of any theoretical (in silico) study of chemical processes. However, a steep increase of the computational cost with increasing system size often prevents the application of accurate approximations to the molecules of interest. The main focus of the present work is the efficient evaluation of Fock-exchange contributions, which typically represents the computational bottleneck in Hartree-Fock (HF) and hybrid density functional theory (DFT) calculations. This bottleneck is addressed by means of seminumerical integration, i.e., one electronic coordinate within the 4-center-2-electron integral tensor is represented analytically and one numerically. In this way, an asymptotically linear scaling method for computing the exchange matrix (denoted as sn-LinK) is developed, enabling fast and accurate ab-initio calculations on large molecules, comprising hundreds or even thousands of atoms, even in combination with large atomic orbital basis sets. The novel sn-LinK method comprises improvements to the numerical integration grids, a rigorous, batch-wise integral screening scheme, the optimal utilization of modern, highly parallel compute architectures (e.g., graphics processing units; GPUs), and an efficient combination of single- and double-precision arithmetic. In total, these optimizations enable over two orders of magnitude faster evaluation of Fock-exchange contributions. Consequently, this greatly improved performance allows to perform previously unfeasible computations, which is also demonstrated at the example of an ab initio molecular dynamics simulation (AIMD) study on the hydrogen bond strengths within double-stranded DNA. In addition to Fock-exchange, the other two computational bottlenecks in hybrid-DFT applications – the evaluation of the Coulomb potential and the numerical integration of the semilocal exchange-correlation functional – are also addressed. Finally, more efficient methods to evaluate more accurate post-HF/DFT methods, namely the random-phase approximation (RPA) and the second-order approximate coupled cluster (CC2) method, are also put forward. In this way, the highly efficient methods introduced in this thesis cover some of the most substantial computational bottlenecks in electronic-structure theory – the evaluation of the Coulomb- and the exchange-interactions, the integration of the semilocal exchange-correlation functional, and the computation of post-Hartree-Fock correlation energies. Consequently, computational chemistry studies on large molecules (>100 atoms) are accelerated by multiple orders of magnitude, allowing for much more accurate and thorough in-silico studies than ever before

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here